U.S. patent application number 10/358039 was filed with the patent office on 2003-08-21 for adaptive method and apparatus for forecasting and controlling neurological disturbances under a multi-level control.
This patent application is currently assigned to The Trustees of the University of pennsylvania. Invention is credited to Echauz, Javier Ramon, Esteller, Rosana, Litt, Brian, Vachtsevanos, George John.
Application Number | 20030158587 10/358039 |
Document ID | / |
Family ID | 24955445 |
Filed Date | 2003-08-21 |
United States Patent
Application |
20030158587 |
Kind Code |
A1 |
Esteller, Rosana ; et
al. |
August 21, 2003 |
Adaptive method and apparatus for forecasting and controlling
neurological disturbances under a multi-level control
Abstract
A method and apparatus for forecasting and controlling
neurological abnormalities in humans such as seizures or other
brain disturbances. The system is based on a multi-level control
strategy. Using as inputs one or more types of physiological
measures such as brain electrical, chemical or magnetic activity,
heart rate, pupil dilation, eye movement, temperature, chemical
concentration of certain substances, a feature set is selected
off-line from a pre-programmed feature library contained in a high
level controller within a supervisory control architecture. This
high level controller stores the feature library within a notebook
or external PC. The supervisory control also contains a knowledge
base that is continuously updated at discrete steps with the
feedback information coming from an implantable device where the
selected feature set (feature vector) is implemented. This high
level controller also establishes the initial system settings
(off-line) and subsequent settings (on-line) or tunings through an
outer control loop by an intelligent procedure that incorporates
knowledge as it arises. The subsequent adaptive settings for the
system are determined in conjunction with a low-level controller
that resides within the implantable device. The device has the
capabilities of forecasting brain disturbances, controlling the
disturbances, or both. Forecasting is achieved by indicating the
probability of an oncoming seizure within one or more time frames,
which is accomplished through an inner-loop control law and a
feedback necessary to prevent or control the neurological event by
either electrical, chemical, cognitive, sensory, and/or magnetic
stimulation.
Inventors: |
Esteller, Rosana; (Marietta,
GA) ; Echauz, Javier Ramon; (Atlanta, GA) ;
Litt, Brian; (Merion Station, PA) ; Vachtsevanos,
George John; (Marietta, GA) |
Correspondence
Address: |
John J. Timar
WOMBLE CARLYLE SANDRIDGE & RICE
POST OFFICE BOX 7037
ATLANTA
GA
30357-0037
US
|
Assignee: |
The Trustees of the University of
pennsylvania
|
Family ID: |
24955445 |
Appl. No.: |
10/358039 |
Filed: |
February 4, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10358039 |
Feb 4, 2003 |
|
|
|
09735364 |
Dec 12, 2000 |
|
|
|
Current U.S.
Class: |
607/45 |
Current CPC
Class: |
A61B 5/4094 20130101;
A61B 5/7267 20130101; G16H 50/20 20180101; A61B 5/7264 20130101;
A61B 5/375 20210101; A61B 5/076 20130101; G16H 20/70 20180101; G16H
50/30 20180101; A61B 5/369 20210101; A61B 5/7275 20130101; G16H
40/63 20180101; A61B 5/7203 20130101; A61B 5/486 20130101; A61N
1/36082 20130101; G06K 9/00523 20130101; A61B 5/726 20130101 |
Class at
Publication: |
607/45 |
International
Class: |
A61N 001/18 |
Claims
What is claimed is:
1. A method for predicting and controlling the electrographic and
clinical onset of a seizure and other neurological events in an
individual, comprising the acts of: generating data that is
acquired from a plurality of input signals obtained from at least
one sensor located in or on the individual; fusing the data to
combine information from the at least one sensor that is connected
to at least one transducer; selecting and extracting a plurality of
features from the fused data; determining from the extracted
features if a seizure or other neurological event is likely to
occur within a plurality of specified time frames, and the
probability of having a seizure for each specified time frame;
providing an alarm to the individual to inform him of an imminent
seizure or neurological event when the probability of seizure is
higher than an adaptive threshold; and applying a control rule to
initiate an intervention measure that is commensurate with the
probability of the electrographical onset of a seizure for each
specified time frame.
2. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 further comprising the act of
normalizing the selected features before determining if a seizure
is likely to occur within the specified time frame.
3. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 further comprising preprocessing of
the input signals to reduce noise, to enhance the quality, to
compensate for undesireable signal variations and to emphasize
distinguishability between a pre-seizure class and a
non-pre-seizure class.
4. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure is
an electrical stimulus of a minimally required duration and
intensity that is delivered at a time that is based on the
probability of seizure for a specified time frame.
5. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure is a
drug infusion that is activated to deliver a minimally required
amount of a drug into the individual at a time that is based on the
probability of seizure for a specified time frame.
6. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure is a
magnetic stimulus generated by the wearing of a magnetic helmet at
a time that is based on the probability of seizure for a specified
time frame.
7. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure is a
procedure that includes the solving of highly cognitive
problems.
8. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure is a
sensory stimulation including at least one of music therapy,
images, flavors, odors and tactile sensations.
9. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure is
delivered in at least one of a region of onset and a distribution
region surrounding the region of offset.
10. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure is
delivered in subcortical regions including at least one of the
thalamus, basal ganglia, and other deep nuclei.
11. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein if the electrograhic onset
occurs, applying treatment to either at least one of a general
region of onset and deep brain structures to modulate the behavior
of the seizure focus.
12. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure
application includes at least one of: rhythmic electrical pacing
that changes in frequency, intensity and distribution as the
probability of a seizure onset reaches and exceeds a threshold;
chaos control pacing; random electrical stimulation to interfere
with developing coherence in activity in a region of, and
surrounding, an epileptic focus; depolarization or
hyperpolarization stimuli to silence or suppress activity in
actively discharging regions, or regions at risk for seizure
spread.
13. The method for predicting and controlling the electrographic
onset of a seizure of claim 12 wherein the intervention measure is
delivered to a plurality of electrodes to provide a surround
inhibition to prevent a progression of a seizure precursor.
14. The method for predicting and controlling the electrographic
onset of a seizure of claim 12 wherein the intervention measure is
delivered sequentially in a wave that covers a cortical or
subcortical region of tissue so as to progressively inhibit normal
or pathological neuronal function in the covered region.
15. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure
application is an infusion of a therapeutic chemical agent into a
brain region where seizures are generated, or to which they may
spread.
16. The method for predicting and controlling the electrographic
onset of a seizure of claim 15 wherein the chemical agent is
delivered in greater quantity, concentration or spatial
distribution as the probability of seizure increases.
17. The method for predicting and controlling the electrographic
onset of a seizure of claim 15 wherein the intervention measure is
applied to at least one of an epilectic focus, an area surrounding
the epilectic focus, a region involved in an early spread, and a
central or deep brain region to modulate seizure propagation.
18. The method for predicting and controlling the electrographic
onset of a seizure of claim 15 wherein the therapeutic chemical
agent is activated by oxidative stress and increases in
concentration and distribution as the probability of seizure
increases.
19. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure is
delivered to central nerves or blood vessels in a graduated manner
as the probability of seizure increases.
20. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the intervention measure is a
plurality of artificial neuronal signals delivered to disrupt
eletrochemical traffic on at least one neuronal network that
includes or communicates with an ictal onset zone.
21. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the alarm is any one of a
visual signal, an audio signal and a tactile sensation.
22. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the plurality of features are
selected for each individual.
23. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the same plurality of
features are selected for each individual.
24. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein parameters of the selected
features are tuned for each individual.
25. The method for predicting and controlling the electrographic
onset of a seizure of claim 24 wherein one of the parameters that
is used for each selected feature is a running window length that
is used in feature extraction.
26. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein a plurality of features are
extracted at an analog level.
27. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein a plurality of features are
extracted at a digital level.
28. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the plurality of features are
extracted over a pre-established window length.
29. The method for predicting and controlling the electrographic
onset of a seizure of claim 28 further comprising shifting of the
window over the plurality of input signals to allow at least a
partial overlap with a previous window, reusing the extracted
features in the overlap portion and repeating the extraction of the
plurality of features on a new input portion within the window.
30. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the act of fusing the data
comprises the act of combining the plurality of signals from at
least one sensor using an intelligent tool including a neural
network or a fuzzy logic algorithm.
31. The method for predicting and controlling the electrographic
onset of a seizure of claim 3 wherein the act of preprocessing of
the input signals comprises subtraction of input signals from
spatially adjacent sensors that measure the same type of
activity.
32. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 wherein the plurality of features is
selected from a feature library including a plurality of historical
and instantaneous features.
33. The method for predicting and controlling the electrographic
onset of a seizure of claim 32 wherein the plurality of
instantaneous features are generated directly from preprocessed and
fused input signals through a running observation window.
34. The method for predicting and controlling the electrographic
onset of a seizure of claim 32 wherein the historical features are
based on a historical evolution of features over time.
35. The method for predicting and controlling the electrographic
onset of a seizure of claim 32 wherein the historical and
instantaneous features are limited to a focus region in the brain
of an individual.
36. The method for predicting and controlling the electrographic
onset of a seizure of claim 32 wherein the historical and
instantaneous features are derived as a spatial feature from a
combination of a plurality of regions in the brain of an
individual.
37. The method for predicting and controlling the electrographic
onset of a seizure of claim 32 wherein the feature library includes
a collection of custom routines to compute the features.
38. The method for predicting and controlling the electrographic
onset of a seizure of claim 32 wherein the plurality of features
are extracted from different domains.
39. The method for predicting and controlling the electrographic
onset of a seizure of claim 38 wherein at least one feature is a
ratio of a short term value and a long term value of that
feature
40. The method for predicting and controlling the electrographic
onset of a seizure of claim 38 wherein the different domains
include at least two of time, frequency, wavelet, fractal geometry,
stochastic processes, statistics, and information theory
domains.
41. The method for predicting and controlling the electrographic
onset of a seizure of claim 40 wherein the time domain features
include at least one of an average power, a power derivative, a
fourth-power indicator, an accumulated energy, an average
non-linear energy, a thresholded non-linear energy, a duration of
thresholded non-linear energy, and a ratio of short term and long
term power feature.
42. The method for predicting and controlling the electrographic
onset of a seizure of claim 41 wherein the fractal geometry
features include at least one of a fractal dimension of analog
signal, a curve length, a fractal dimension of digital signals, a
ratio of short term and long term curve length, an a ratio of short
term and long term fractal dimensions of digital signals.
43. The method for predicting and controlling the electrographic
onset of a seizure of claim 41 wherein the frequency domain
features include at least one of a power spectrum, a power on
frequency bands, a coherence between intracranial channels, a mean
crossings and a zero crossings feature.
44. The method for predicting and controlling the electrographic
onset of a seizure of claim 41 wherein the wavelet domain features
include at least one of a spike detector, a density of spikes over
time, and an absolute value of a wavelet coefficient.
45. The method for predicting and controlling the electrographic
onset of a seizure of claim 41 wherein the statistics and
stochastic process domains include at least one of a mean frequency
index, a cross-correlation between different intracranial channels,
and autoregressive coefficients.
46. The method for predicting and controlling the electrographic
onset of a seizure of claim 41 wherein the information theory
features include at least one of an entropy feature and an average
mutual information feature.
47. The method for predicting and controlling the electrographic
onset of a seizure of claim 34 wherein at least one historical
feature is generated as a feature of other features by a second or
higher level of feature extraction.
48. The method for predicting and controlling the electrographic
onset of a seizure of claim 25 wherein a determination of the
running window length and a starting time for feature extraction
over an input signal for every feature includes the acts of:
determining a window range based on stationarity criteria and a
minimum length to compute a feature under analysis; determining a
feature value for each of a plurality of different window sizes,
calculating a feature effectiveness measure based on class
distinguishability for the plurality of different window sizes used
for every feature; determining the window length that corresponds
to a best class distinguishability as indicated by a maximum value
or minimum value of the feature effectiveness measure; and aligning
the plurality of windows with the window having the maximum length
such that the right edge of all windows coincide.
49. The method for predicting and controlling the electrographic
onset of a seizure of claim 48 wherein the maximum or minimum
values of the feature effectiveness measure that provides the best
class distinguishability depends on the feature effectiveness
measure in use.
50. The method for predicting and controlling the electrographic
onset of a seizure of claim 48 wherein the feature effectiveness
measure determines the window length that maximizes the
distinguishability between a preictal/ictal class and a baseline
class.
51. The method for predicting and controlling the electrographic
onset of a seizure of claim 50 wherein the act of selecting and
extracting a plurality of features comprises the acts of:
extracting a set of candidate features from the feature library;
ranking the extracted features by the feature effectiveness
measure; and determining a smallest subset of features that
satisfies a performance criterion.
52. The method for predicting and controlling the electrographic
onset of a seizure of claim 51 further comprising the acts of:
performing an initial pre-selection from the feature library to
discard a plurality of features with inferior class separability;
and evaluating individual feature performance using at least one
criterion for every feature that is not discarded during the
initial pre-selection.
53. The method for predicting and controlling the electrographic
onset of a seizure of claim 51 wherein the act or ranking the
extracted features by the feature effectiveness measure uses an
overlap measure criterion, a modified add-on algorithm and
heuristics to select a final feature set.
54. The method for predicting and controlling the electrographic
onset of a seizure of claim 51 further comprising the acts of
constructing and evaluating two-dimensional feature spaces to
validate qualitatively that the final feature set is complementary
and has low correlation among the final features.
55. The method for predicting and controlling the electrographic
onset of a seizure of claim 53 wherein the overlap measure
criterion is based on functions proportional to the estimated
conditional probability distributions of the features under
analysis for both a pre-seizure class and a non-pre-seizure
class.
56. The method for predicting and controlling the electrographic
onset of a seizure of claim 30 wherein the neural network or fuzzy
logic algorithm include at least one of a probabilistic neural
network, a k-nearest neighbor neural network, a wavelet network,
and a combination probabilistic/k-nearest neighbor neural
network.
57. The method for predicting and controlling the electrographic
onset of a seizure of claim 3 wherein the act of preprocessing the
input signals comprises classification of an individual's awareness
state within at least one of the categories of awake, asleep, and
drowsy using algorithms based on frequency and time
information.
58. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 further comprising the act of fusing
the selected features to include establishing an individual-tuned
variable normalization level that uses an individual's state of
awareness to normalize an accumulated energy or other feature and
decide if a seizure is approaching when a normalized threshold
value is exceeded.
59. A computer readable medium containing a computer program
product for predicting and controlling the electrographic and
clinical onset of a seizure and other neurological events in an
individual, the computer program product comprising: program
instructions that generate data acquired from a plurality of input
signals obtained from at least one sensor located in or on the
individual; program instructions that fuse the data to combine
information from the at least one sensor that is connected to at
least one transducer; program instructions that select and extract
a plurality of features from the fused data; program instructions
that determine from the extracted features if a seizure or other
neurological event is likely to occur within a plurality of
specified time frames, and the probability of having a seizure for
each specified time frame; program instructions that generate an
alarm to the individual to inform him of an imminent seizure or
neurological event when the probability of seizure is higher than
an adaptive threshold; and program instructions that apply a
control rule to initiate an intervention measure that is
commensurate with the probability of the electrographical onset of
a seizure.
60. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that initiate a preproccessing of the input
signals to reduce noise and to enhance the quality, and to
emphasize distinguisability between a pre-seizure class and a
non-pre-seizure class.
61. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that initiate an electrical stimulus of a
minimally required duration and intensity that is delivered at a
time that is based on the probability of seizure for a specified
time frame.
62. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that initiate activation of a drug infusion to
deliver a minimally required amount of a drug into the individual
at a time that is based on the probability of a seizure for a
specified time frame.
63. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that initiate generation of a magnetic
stimulus through the wearing of a magnetic helmet at a time that is
based on the probability of seizure for a specified time frame.
64. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that provide an indication that a cognitive
problem should be solved as an intervention measure.
65. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that provide an indication that a sensory
stimulation should be applied as an intervention measure.
66. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that initiate activation of any one of a
visual alarm, an audio alarm, and a tactile sensation.
67. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that select a plurality of features for each
individual.
68. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that select the same plurality of features for
each individual.
69. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that tune the parameters of the selected
features for each individual.
70. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that determine a running window length which
is used in feature extraction for each selected feature.
71. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that extract a plurality of features at an
analog level.
72. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that extract a plurality of features at a
digital level.
73. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 70 further comprising
program instructions that extract a plurality of features over a
preestablished window length.
74. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 73 further comprising
program instructions that shift the window over the plurality of
input signals to allow at least a partial overlap with a previous
window and repeat the extraction of the plurality of features.
75. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that combine the plurality of signals from at
least one sensor using an intelligent tool that includes a neural
network or a fuzzy logic algorithm.
76. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 60 wherein the program
instruction for preprocessing of the input signals further
comprises program instructions that subtract the signals from
spatially adjacent sensors that measure the same type of
activity.
77. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 59 further comprising
program instructions that select a plurality of features from a
feature library that includes a plurality of historical and
instantaneous features.
78. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 77 further comprising
program instructions that generate a plurality of instantaneous
features directly from pre-processed and fused input signals
through a running observation window.
79. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 77 further comprising
program instructions that generate historical features based on a
historical evolution of features over time.
80. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 77 further comprising
program instructions that limit the historical and instantaneous
features to a focus region in the brain of an individual.
81. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 77 further comprising
program instructions that derive historical and instantaneous
features as a spatial feature from a combination of a plurality of
regions in the brain of an individual.
82. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 77 further comprising
program instructions collected as custom routines within the
feature library to compute the features.
83. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 77 further comprising
program instructions that extract a plurality of features from
different domains.
84. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 83 further comprising
program instructions that determine at least one feature as a ratio
of a short term value and a long term value of that feature.
85. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 83 wherein the different
domains include at least two of time, frequency, wavelet, fractal
geometry, stochastic processes, statistics, and information theory
domains.
86. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 85 further comprising
program instructions that determine at least one of an average
power, a power derivative, a fourth-power indicator, an accumulated
energy, and average non-linear energy, a thresholded non-linear
energy, a duration of thresholded non-linear energy, and a ratio of
short term and long term power as time domain features.
87. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 86 further comprising
program instructions that determine at least one of a fractal
dimension of analog signals, a curve length, a fractal dimension of
digital signals, a ratio of a short term and a long term fractal
dimension of digital signals, and a ratio of short term and long
term curve length as fractal geometry features.
88. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 86 further comprising
program instructions that determine at least one of a power
spectrum, a power on frequency bands, a coherence between
intracranial channels, a mean crossings and a zero crossings
feature.
89. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 86 further comprising
program instructions that determine at least one of a spike
detector, a density of spikes over time, and an absolute value of a
wavelet coefficient as wavelet domain features.
90. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 86 further comprising
program instructions that determine at least one of a mean
frequency index, a cross-correlation between different intracranial
channels, and autoregressive coefficients as features in the
statistics and stochastic process domains.
91. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 86 further comprising
program instructions that determine at least one of an entropy
feature and an average mutual information feature as information
theory features.
92. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 70 wherein the program
instructions for determining the running window length further
comprise: program instructions that determine a window range based
on stationarity criteria and a minimum length to compute a feature
under analysis; program instructions that determine a feature value
for each of a plurality of different window sizes; program
instructions that calculate a feature effectiveness measure for
each feature for the plurality of different window sizes; program
instructions that determine the optimal window length for each
feature from the plurality of windows examined that corresponds to
a value of the feature effectiveness measure wherein the
distinguisability between a preictal class and a non-preictal class
is maximized; and program instructions that align the plurality of
optimal windows determined for each feature with the feature window
having the maximum length.
93. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 92 further comprising
program instructions that initiate re-execution of the program
instructions that determine a feature value and the program
instructions that calculate a feature effectiveness measure for
each selected feature.
94. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 92 further comprising
program instructions that maximize the distinguishability between a
preictal/ictal class and a baseline class as the feature
effectiveness measure.
95. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 94 wherein the program
instructions that select and extract a plurality of features
comprise: program instructions that extract a set of candidate
features from the feature library; program instructions that rank
the extracted features by the feature effectiveness measure; and
program instructions that determine a smallest subset of features
that satisfies a performance criterion.
96. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 95 further comprising:
program instructions that perform an initial pre-selection from the
feature library to discard a plurality of features with inferior
class separability; and program instructions that evaluate
individual feature performance using at least one criterion for
every feature that is not discarded during the initial
pre-selection.
97. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 95 wherein the program
instructions that rank the extracted features by the feature
effectiveness measure use an overlap measure criterion, a modified
add-on algorithm and heuristics to select a final feature set.
98. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 97 further comprising
program instructions that construct and evaluate two-dimensional
feature spaces to validate qualitatively that the final feature set
is complementary and has low correlation among the final
features.
99. The computer program product for predicting and controlling the
electrographic onset of a seizure of claim 97 further comprising
program instructions that base the overlap measure criterion on
estimated conditional probability distributions of each particular
feature under analysis for both a pre-seizure class and
non-pre-seizure class.
100. The computer program product for predicting and controlling
the electrographic onset of a seizure of claim 75 further
comprising program instructions that determine at least one of a
probabilistic neural network, a k-nearest neighbor neural network,
a wavelet network, and a combination probabilistic/k-nearest
neighbor neural network.
101. The computer program product for predicting and controlling
the electrographic onset of a seizure of claim 60 wherein the
program instructions for preprocessing of the input signals further
comprises program instructions that classify an individual's
awareness state within at least one of the categories of awake,
asleep, and drowsy.
102. The computer program product for predicting and controlling
the electrographic onset of a seizure of claim 101 wherein the
program instructions that classify an individual's awareness state
within the categories of awake, asleep and drowsy are based on
frequency and time information.
103. The computer program product for predicting and controlling
the electrographic onset of a seizure of claim 59 further
comprising program instructions that fuse the selected features to
include establishing an individual-tuned variable normalization
level that uses an individual's state of awareness to normalize an
accumulated energy or other feature and decide if a seizure is
approaching when a normalized threshold value is exceeded.
104. A system for predicting and controlling the electrographic and
clinical onset of a seizure and other neurological disturbances in
an individual, comprising: a data generation component to acquire
physiological signals from the individual; an intelligent data
processing unit to preprocess the physiological signals, to extract
and select a plurality of features, and to provide an estimation of
the probability of seizure for at least one time frame; and a low
level controller connected to the intelligent data processing unit
to automatically activate a therapeutic intervention measure to
control the onset of a seizure in the individual in response to the
probability of seizure exceeding a threshold.
105. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 further comprising at least one
sensor for detecting physiological signals that indicate the state
of activity in the brain of the individual.
106. The system for predicting and controlling the electrographic
onset of a seizure of claim 105 wherein the sensor is at least one
of an implanted intracranial electrode, an epidural electrode, a
scalp electrode, a sphenoidal electrode, a foramen ovale electrode,
an intravascular electrode, a chemical sensor, a pupil dilation
sensing device, an eye movement sensor, a heart rate sensor, and a
body temperature sensor.
107. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 further comprising a high level
controller that communicates with the intelligent data processing
unit to retune at least one parameter that is used to extract and
select a feature.
108. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 further comprising an external
portable module including an external communications unit that
enables the transfer of physiological data that is sensed in the
individual to the external portable module for analysis and
storage.
109. The system for predicting and controlling the electrographic
onset of a seizure of claim 108 wherein the external portable
module further comprises a display device that shows the
probability output from the intelligent data processing unit for
having a seizure in at least one time frame.
110. The system for predicting and controlling the electrographic
onset of a seizure of claim 108 wherein the external portable
module further comprises an alarm device which is activated to
alert the individual of an oncoming seizure when the probability of
having a seizure in at least one time frame exceeds an adaptive
threshold.
111. The system for predicting and controlling the electrographic
onset of a seizure of claim 108 wherein the external portable
module further comprises a battery recharger.
112. The system for predicting and controlling the electrographic
onset of a seizure of claim 108 wherein the external portable
module further comprises at least one of a microprocessor, a
digital signal processor, a field programmable gate array, and an
application specific integrated circuit.
113. The system for predicting and controlling the electrographic
onset of a seizure of claim 108 wherein the external communications
unit communicates with the intelligent data processing unit by any
one of telemetry, magnetic induction, direct electrical connection,
optical communication and ultrasonic communication.
114. The system for predicting and controlling the electrographic
onset of a seizure of claim 108 wherein the external portable
module further comprises a communications port that enables the
external portable module to be connected to a serial or a parallel
port of a computer system, and that enables the transmission of
stored data from the external portable module through an Internet
connection to another computer system where the transmitted data
can be downloaded and stored.
115. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 wherein the intelligent data
processing unit is contained in an implantable device.
116. The system for predicting and controlling the electrographic
onset of a seizure of claim 115 wherein the implantable device is
implanted in the brain of the individual.
117. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 wherein the intelligent data
processing unit is programmed into any one of a microprocessor, a
digital signal processor, a field programmable gate array, and an
application specific integrated circuit (ASIC) embedded on a
microchip.
118. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 wherein the intelligent data
processing unit comprises a preprocessor to amplify and filter the
physiological signals.
119. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 wherein the intelligent data
processing unit comprises a first feature extraction module to
extract analog features from the preprocessed physiological
signals.
120. The system for predicting and controlling the electrographic
onset of a seizure of claim 119 wherein the intelligent data
processing unit further comprises a second feature extraction
module to extract digital features from the preprocessed
physiological signals.
121. The system for predicting and controlling the electrographic
onset of a seizure of claim 120 wherein the intelligent data
processing unit further comprises a feature vector generator module
that combines a plurality of extracted features based on a running
window technique.
122. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 wherein the intelligent data
processing unit comprises an on-board memory to record the
physiological signals over a period of time based on a capacity of
the memory.
123. The system for predicting and controlling the electrographic
onset of a seizure of claim 121 wherein the intelligent data
processing unit further comprises an intelligent prediction
analysis and classification module operating on a central processor
that analyzes the feature vector to provide an estimation of the
probability of having a seizure for one or more time frames.
124. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 further comprising a neural network
to perform the analysis of the feature vector.
125. The system for predicting and controlling the electrographic
onset of a seizure of claim 124 wherein the neural network is at
least one of a probabilistic neural network, a k-nearest neighbor
neural network, and a wavelet neural network.
126. The system for predicting and controlling the electrographic
onset of a seizure of claim 123 further comprising an internal
communications unit to enable the transfer of physiological data
that is sensed in the individual by a central processor in the
intelligent data processing unit to an external portable module
that displays the probability of seizure for at least one time
frame.
127. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 further comprising an internal
electrical stimulation unit activated by the low level controller
to electrically stimulate focal points to prevent synchronized
nerve impulses as the therapeutic intervention measure.
128. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 further comprising a drug delivery
system activated by the low level controller to provide chemical
stimulation as by releasing small quantities of a drug as the
therapeutic intervention measure.
129. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 further comprising a special helmet
activated by the low level controller to provide magnetic
stimulation as the therapeutic intervention measure.
130. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 wherein the low level controller
activates a stimulation unit to instruct the individual to initiate
a sensory/perceptive stimulus as the therapeutic intervention
measure.
131. The system for predicting and controlling the electrographic
onset of a seizure of claim 104 wherein the low level controller
activates a stimulation unit to instruct the individual to initiate
a cognitive stimulus as the therapeutic intervention measure.
132. The system for predicting and controlling the electrographic
onset of a seizure of claim 130 wherein the sensory/perceptive
stimulus is any of a visual, an auditory, a tactile, a smell and a
taste stimulus.
133. The system for predicting and controlling the electrographic
onset of a seizure of claim 131 wherein the cognitive stimulus is
any of a reading, a mathematical computation, and a logic reasoning
problem stimulus.
134. An adaptive multi-level hierarchical control system for
predicting and controlling the electrographic onset of a seizure
and other neurological disturbances in an individual, comprising: a
data generation component that acquires physiological signals from
the individual; an intelligent data processing device that
processes the physiological signals to extract features which are
analyzed and classified and selected to form a feedback vector; a
low level controller including a stimulation device that is
activated to apply an intervention measure in response to the
feedback vector to control the onset of seizure and to adjust
internal parameter settings of the actuators in the stimulation
device; and a high level supervisory controller including a
knowledge database and a processor that adapts to feedback vector
changes over time and re-tunes the intelligent data processing
device parameter settings dynamically.
135. The adaptive multi-level hierarchical control system of claim
134 wherein the knowledge base comprises a priori information for
the individual.
136. The adaptive multi-level hierarchical control system of claim
135 wherein the a priori information for the individual comprises
seizure frequency over time, seizure duration, type of seizure, and
aura frequency collected before an implantation of the intelligent
data processing device.
137. The adaptive multi-level hierarchical control system of claim
134 wherein the low level controller determines and adjusts the
parameter settings of the actuators in the stimulation device
continuously.
138. The adaptive multi-level hierarchical control system of claim
134 wherein the high level supervisory controller can operate in an
automatic mode or in a semi-automatic mode.
139. The adaptive multi-level hierarchical control system of claim
138 further comprising a master program that monitors a set of
controlled variables and updates the applied feedback control laws
when operating in the automatic mode.
140. The adaptive multi-level hierarchical control system of claim
138 wherein a physician or specialist inputs parameters directly
into the intelligent data processing device through a master
program user interface when operating in a semi-automatic mode.
141. The adaptive multi-level hierarchical control system of claim
134 wherein the high level supervisory controller is a computer
external to the intelligent data processing device for providing a
coordination layer control.
142. The adaptive multi-level hierarchical control system of claim
141 wherein the coordination layer of control returns system
parameters including parameters related to fusion of sensory data,
feature extraction, feature normalization, neural network
retraining, fuzzy logic adjustments, and fault diagnoses of
actuators, sensors and implantable device.
143. The adaptive multi-level hierarchical control system of claim
134 further comprising an external computer for providing a
research layer control to evaluate any new algorithms for control
of seizures or brain disturbances, for prediction and detection of
the unequivocal electrographic onset of seizure, for control
strategies, or for other types of parameter adjustments.
144. The adaptive multi-level hierarchical control system of claim
143 wherein the research layer computer analyzes physiological
mechanisms to explain seizure and other brain disturbances.
145. The adaptive multi-level hierarchical control system of claim
143 wherein the research layer collects information from a
plurality of individuals to form a research and development
database.
146. The adaptive multi-level hierarchical control system of claim
134 wherein the multi-level hierarchical control is provided by a
feedback control law updated by the low level controller and a
knowledge base control law updated by the high level supervisory
controller.
147. The adaptive multi-level hierarchical control system of claim
146 wherein the adaptive hierarchical control is provided by the
updated knowledge base control law.
148. The adaptive multi-level hierarchical control system of claim
134 wherein the processor for the high level supervisory controller
operates a logic module that executes optimization algorithms and
determines self-evaluation metrics to establish the supervisory
controller's performance over time, to determine required
adjustments in the intelligent data processing device's set points,
and to generate an updated feedback control law that is downloaded
into the intelligent data processing device.
149. The adaptive multi-level hierarchical control system of claim
134 wherein the knowledge database is updated at discrete steps by
downloading new information from the intelligent data processing
device.
150. The adaptive multi-level hierarchical control system of claim
134 further comprising an external portable module including an
external communications unit that enables the transfer of
physiological data that is sensed in the individual to the external
portable module for analysis and storage.
151. The adaptive multi-level hierarchical control system of claim
150 wherein the external portable module further comprises a
display device that shows the probability from the intelligent data
processing unit for having a seizure in at least one time
frame.
152. The adaptive multi-level hierarchical control system of claim
150 wherein the external portable module further comprises an alarm
device which is activated to alert the individual of an oncoming
seizure when the probability of having a seizure in at least one
time frame exceeds an adaptive threshold.
153. The adaptive multi-level hierarchical control system of claim
134 wherein the intelligent data processing device is implanted
into the individual.
154. The adaptive multi-level hierarchical control system of claim
153 wherein the intelligent data processing device includes a
learning capability based on artificial intelligence tools and an
analysis of previously stored information that enables an
adaptation of the intelligent data processing device to the
individual in which it is implanted and a specific state of the
individual at any time.
155. The adaptive multi-level hierarchical control system of claim
134 further comprising at least one sensor for detecting
physiological signals that indicate the state of activity in the
brain of an individual.
156. A method for predicting and controlling the electrographic
onset of a seizure in an individual using a multi-level
hierarchical control system including an implanted device,
comprising the acts of: installing at least one sensor on or in the
individual to detect input signals indicative of brain activity;
implanting the device into the brain of the individual;
initializing and tuning a plurality of parameters in the implanted
device; installation of an external portable module that contains
an external communications unit, a settings adjustment unit with a
display and a keypad and an intermediate storage device; selecting
features to extract from the input signals; analyzing and
classifying the selected features extracted from the input signals
in order to predict the probability of having a seizure in a
plurality of time frames; activating a closed-loop control system
in the implanted device through the external portable module; and
applying a multi-level control to the implanted device to initiate
an intervention measure that is based on the probability of having
a seizure in a plurality of time frames.
157. The method for predicting and controlling the onset of a
seizure of claim 156 wherein the implanted device is
feature/parameter-tuned with features that are selected for each
patient based on the features that can capture the unequivocal
electrographic onset of seizure in advance.
158. The method for predicting and controlling the onset of a
seizure of claim 156 wherein the implanted device is
parameter-tuned with the same features used for each individual
receiving an implanted device in which the parameters are tuned on
an individual basis.
159. The method for predicting and controlling the onset of a
seizure of claim 156 wherein the act of installing the at least one
sensor includes determining the focus region for correct
installation.
160. The method for predicting and controlling the onset of a
seizure of claim 156 wherein the act of initializing the parameter
settings includes the acts of: recording sensor data into the
intermediate storage device continuously from a pair of input
channels; downloading the recorded sensor data from the
intermediate storage device into an external processing device;
preprocessing and fusing the downloaded sensor data by the external
processing device; extracting and selecting features in the
external processing device; selecting a best feature set by the
external processing device to establish a feature vector; and
transferring and setting the selected feature algorithms from the
external processing device into the implantable device.
161. The method for predicting and controlling the onset of a
seizure of claim 156 wherein the acts of analyzing and classifying
the selected features includes the acts of: performing real-time
processing of the input signals from the at least one sensor by
subtracting a focal channel input signal from an adjacent channel,
and filtering the difference signal; extracting each selected
feature at an analog level or a digital level based on the
characteristics of the selected feature; combining the extracted
features using a running-window technique to generate a feature
vector; normalizing the feature vector by a processor in the
implanted device; performing analysis of the feature vector for
each time frame using a fuzzy system or a neural network to provide
an estimation of the probability of having a seizure for at least
one time frame.
162. The method for predicting and controlling the onset of a
seizure of claim 161 further comprising the acts of: displaying a
probability output of having a seizure for at least one time frame
on the display of the external portable module; and activating an
alarm to alert the individual of an oncoming seizure when the
probability output exceeds an adaptive threshold.
163. The method for predicting and controlling the onset of a
seizure of claim 161 further comprising the acts of: scheduling the
download of recorded sensor data from a buffer in the implanted
device into the intermediate storage device by a processor in the
external portable module; transferring data between the external
processing device and the external portable module to establish
supervisory control actions and to communicate the control actions
to the implanted device.
164. The method for predicting and controlling the onset of a
seizure of claim 163 further comprising the act of establishing a
communications link between a central processor in the implanted
device and the processor in the external portable module.
165. The method for predicting and controlling the onset of a
seizure of claim 161 further comprising the act of recording
physiological input signals in an internal buffer of the implanted
device for a period of time that depends on the memory capability
of the buffer.
166. The method for predicting and controlling the onset of a
seizure of claim 165 further comprising the act of downloading
physiological input signals, the feature vector and a plurality of
controlled variables from the internal buffer to the intermediate
storage device via a communications link.
167. The method for predicting and controlling the onset of a
seizure of claim 166 further comprising the act of downloading data
from the intermediate storage device to the external processing
device.
168. The method for predicting and controlling the onset of a
seizure of claim 161 further comprising the act of performing an
initial adaptation of the implanted device at periodically discrete
times by connecting the external portable module to a high level
supervisory control in the external processing device.
169. The method for predicting and controlling the onset of a
seizure of claim 156 wherein the act of applying a multi-level
control includes the acts of: activating the closed-loop control
system via a high level supervisory control through the external
portable module; generating feedback control signals by the low
level controller to prevent seizures by producing an intermittent
electrical, chemical or a magnetic stimulation; estimating
prediction and prevention performance by evaluating a plurality of
key parameters; computing an overall performance metric from the
prediction and prevention performance; adjusting the parameters of
a stimulation device and determining a type of stimulation to apply
and a corresponding start time, intensity, duration and frequency;
updating feedback control and knowledge base laws; adapting the
feedback control laws to internal and external changes over time to
prevent seizure with less-invasive intervention measures; and
tuning internal feature parameters and analysis and classification
parameters adaptively based on the combined information contained
in the feedback control signals and the overall performance
measures.
170. The method for predicting and controlling the onset of a
seizure of claim 169 further comprising the acts of: activating an
input channel by the individual via the keypad in the external
portable module; automatically adjusting the hierarchical control
system in response to the activation of an input channel; assessing
hierarchical control system performance by using information
regarding the probability of seizure in conjunction with preictal
and ictal recorded data.
171. The method for predicting and controlling the onset of a
seizure of claim 170 wherein the hierarchical control system
performance evaluation is performed automatically at a regulatory
feedback control level and at a high level supervisory
controller.
172. The method for predicting and controlling the onset of a
seizure of claim 170 wherein the hierarchical control system
performance is activated by an authorized person.
173. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 further comprising the act of
implanting a plurality of electrodes in each focus region of the
individual.
174. The method for predicting and controlling the electrographic
onset of a seizure of claim 173 wherein the act of fusing the data
comprises subtracting the input signals from adjacent electrodes to
form a bipolar signal, and combining the bipolar signals from
different focus regions at the data level.
175. The method for predicting and controlling the electrographic
onset of a seizure of claim 173 wherein the act of fusing the data
comprises subtracting the input signals from adjacent electrodes to
form a bipolar signal, and combining the bipolar signals from
different focus regions at the feature level.
176. The method for predicting and controlling the electrographic
onset of a seizure of claim 174 wherein the input signals are
combined into a signal data stream either before or after a
preprocessing stage.
177. The method for predicting and controlling the electrographic
onset of a seizure of claim 176 wherein the input signals are
intracranial electroencephalogram data.
178. The method for predicting and controlling the electrographic
onset of a seizure of claim 175 wherein the features derived from
the input signals and coincident or aligned in time are combined
into a single feature using a nonlinear procedure.
179. The method for predicting and controlling the electrographic
onset of a seizure of claim 178 wherein the nonlinear procedure
comprises selecting the maximum value of the input signals at each
sample time.
180. The method for predicting and controlling the electrographic
onset of a seizure of claim 1 further comprising the act of
implanting a plurality of electrodes in a unique focus region and
in at least one other region of the brain of the individual.
181. The method for predicting and controlling the electrographic
onset of a seizure of claim 180 wherein the at least one other
region is a focal adjacent channel.
182. The method for predicting and controlling the electrographic
onset of a seizure of claim 180 wherein the act of fusing the data
comprises subtracting the input signals from a pair of electrodes
placed in different regions to form a bipolar signal, and combining
a plurality of bipolar signals at the data level.
183. The method for predicting and controlling the electrographic
onset of a seizure of claim 180 wherein the act of fusing the data
comprises subtracting the input signals from a pair of electrodes
placed in different regions to form a bipolar signal, and combining
a plurality of bipolar signals at the feature level.
184. The method for predicting and controlling the electrographic
onset of a seizure of claim 182 wherein the input signals are
combined into a signal data stream either before or after a
preprocessing stage.
185. The method for predicting and controlling the electrographic
onset of a seizure of claim 184 wherein the input signals are
intracranial electroencephalogram data.
186. The method for predicting and controlling the electrographic
onset of a seizure of claim 183 wherein the features derived from
the input signals and coincident or aligned in time are combined
into a single feature using a nonlinear procedure.
187. The method for predicting and controlling the electrographic
onset of a seizure of claim 186 wherein the nonlinear procedure
comprises selecting the maximum value of the input signals at each
sample time.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is related to co-pending patent application
"Unified Probabilistic Framework For Predicting and Detecting
Seizure Onsets In The Brain and Multitherapeutic Device", Ser. No.
09/693423, filed Oct. 20, 2000. The present application is also
related to international application WO 00/10455, published under
the Patent Cooperation Treaty (PCT) on Mar. 2, 2000. The related
patent applications are hereby incorporated by reference into this
description as fully as if here represented in full.
BACKGROUND OF THE INVENTION
[0002] The present invention is in the field of prediction and
control of neurological disturbances, particularly in the area of
electrographic and clinical seizure onset prediction based on
implantable devices with the major goal of alerting and/or avoiding
seizures.
[0003] Approximately 1% of the world's population has epilepsy, one
third of whom have seizures not controlled by medications. Some
patients, whose seizures reliably begin in one discrete region,
usually in the mesial (middle) temporal lobe, may be cured by
epilepsy surgery. This requires removing large volumes of brain
tissue, because of the lack of a reliable method to pinpoint the
location of seizure onset and the pathways through which seizures
spread. The 25% of refractory patients in whom surgery is not an
option must resort to inadequate treatment with high doses of
intoxicating medications and experimental therapies, because of
poorly localized seizure onsets, multiple brain regions
independently giving rise to seizures, or because their seizures
originate from vital areas of the brain that cannot be removed. For
these and all other epileptic patients, the utilization of a
predicting device would be of invaluable help. It could prevent
accidents and allow these patients to do some activities that
otherwise would be risky.
[0004] Individuals with epilepsy suffer considerable disability
from seizures and resulting injuries, impairment of productivity,
job loss, social isolation associated with having seizures,
disabling side effects from medications and other therapies. One of
the most disabling aspects of epilepsy is that seizures appear to
be unpredictable. However, in this invention a seizure prediction
system is disclosed. Seizure prediction is a highly complex problem
that involves detecting invisible and unknown patterns, as opposed
to detecting visible and known patterns involved in seizure
detection. To tackle such an ambitious goal, some research groups
have begun developing advanced signal processing and artificial
intelligence techniques. The first natural question to ask is in
what ways the preictal (i.e., the period preceding the time that a
seizure takes place) intracranial EEGs (IEEGs) are different from
all other IEEGs segments not immediately leading to seizures. When
visual pattern recognition is insufficient, quantitative EEG
analysis may help extract relevant characteristic measures called
features, which can then be used to make statistical inferences or
to serve as inputs in automated pattern recognition systems.
[0005] Typically, the study of an event involves the goals of
diagnosing (detecting) or prognosticating (predicting) such event
for corrective or preventive purposes, respectively. Particularly,
in the case of brain disturbances such as epileptic seizures, these
two major goals have driven the efforts in the field. On one hand,
there are several groups developing seizure detection methods to
implement corrective techniques to stop seizures, and on the other,
there are some groups investigating seizure prediction methods to
provide preventive ways to avoid seizures. Among the groups
claiming seizure prediction, three categories of prediction can be
distinguished, clinical onset (CO) prediction, electrographic onset
(EO) prediction studies, and EO prediction systems. All these
categories in conjunction with seizure detection compose most of
the active research in this field.
[0006] Related art approaches have focused on nonlinear methods
such as studying the behavior of the principal Lyapunov exponent
(PLE) in seizure EEGs, computing a correlation dimension or
nonlinear chaotic analysis or determining one major feature
extracted from the ictal characteristics of an electroencephalogram
(EEG) or electrocorticogram (ECoG).
[0007] Important Terminology Definitions
[0008] Ictal period: time when the seizure takes place and
develops.
[0009] Preictal period: time preceding the ictal period.
[0010] Interictal period or baseline: period at least 1 hour away
from a seizure. Note that the term baseline is generally used to
denote "normal" periods of EEG activity, however, in this invention
it is used interchangeably with interictal period.
[0011] Clinical onset (CO): the time when a clinical seizure is
first noticeable to an observer who is watching the patient.
[0012] Unequivocal Clinical onset (UCO): the time when a clinical
seizure is unequivocally noticeable to an observer who is watching
the patient.
[0013] Unequivocal Electrographic Onset (UEO): also called in this
work electrographic onset (EO), indicates the unequivocal beginning
of a seizure as marked by the current "gold standard" of expert
visual analysis of the IEEG.
[0014] Earliest Electrographic Change (EEC): the earliest change in
the intracranial EEG (IEEG) preceding the UEO and possibly related
to the seizure initiation mechanisms.
[0015] Focus Channel: the intracranial EEG channel where the UEO is
first observed electrographically.
[0016] Focal Adjacent Channel: the intracranial EEG channels
adjacent to the focus channel.
[0017] Focus Region: area of the brain from which the seizures
first originate.
[0018] Feature: qualitative or quantitative measure that distills
preprocessed data into relevant information for tasks such as
prediction and detection.
[0019] Feature library: collection of algorithms used to determine
the features.
[0020] Feature vector: set of selected features used for prediction
or detection that forms the feature vector.
[0021] Aura: symptom of a brain disturbance usually preceding the
seizure onset that may consist of hallucinations, visual illusions,
distorted understanding, and sudden, intense emotion, such as
anxiety or fear.
[0022] FIGS. 11A-11B illustrate some of the defined terms on
segments of a raw IEEG signal. Comparison between the preictal
segment indicated on FIG. 11A (between the EEC and the UEO times)
and the interictal period in FIG. 11B demonstrates the difficulty
of discerning between them. The vertical scale in both figures is
in microvolts (.mu.V).
SUMMARY OF THE INVENTION
[0023] This invention is an automatic system that predicts or
provides early detection of seizure onsets or other neurological
events or disturbances with the objective of alerting, aborting or
preventing seizures or other neurological ailments by appropriate
feedback control loops within multiple layers. One of the main
differences from other inventions is that the major functions of
the brain implantable device is forecasting and preventing seizures
or other brain disturbances rather than only detecting them. Unlike
other inventions, the goal is to predict the electrographic onset
of the disturbance or seizure rather than the clinical onset.
Seizure UEO detection is also accomplished as a direct consequence
of the prediction and as a means to assess device performance.
Furthermore, the innovative presence of a supervisory control
provides the apparatus with a knowledge updating capability
supported by the external PC or notebook, and a self-evaluation
proficiency used as part of the feedback control to tune the device
parameters at all stages, also not present in the other art.
[0024] The approach disclosed in the present invention, instead of
focusing on nonlinear methods, or on one particular feature,
targets multiple features from different domains and combines them
through intelligent tools such as neural networks and fuzzy logic.
Multiple and synergistic features are selected to exploit their
complementarity. Furthermore, rather than using a unique crisp
output that considers one particular time frame, as the previous
methods introduced, the system provides one or more probabilistic
outputs of the likelihood of having a seizure within one or more
time frames. Based on this, when a threshold probability is
reached, an approaching seizure can be declared. The use of these
multiple time frames and probabilistic outputs are other distinct
aspects from previous research in the field.
[0025] The system possesses multiple levels of closed-loop control.
Low-level controls are built up within the implantable device, and
consist of brain stimulation actuators with their respective
feedback laws. The low-level control operates in a continuous
fashion as opposed to previous techniques that provide only one
closed-loop control that runs only during short times when the
seizure onset is detected. The high-level control is performed by a
supervisory controller which is achieved through an external PC or
notebook. By using sophisticated techniques, the prediction system
envisioned allows the patients or observers to take appropriate
precautions before the seizure onset to avoid injuries.
Furthermore, the special design of the apparatus furnishes powerful
techniques to prevent or avoid seizures and to obtain more insight
into these phenomena, thereby revealing important clinical
information. The innovative use of a supervisory control is the
option that confers the apparatus its unique perspective as a
warning/control/adaptive long-term device. The warning is achieved
by forecasting the disturbance; the control is accomplished by an
appropriate feedback law and a knowledge base update law; and the
adaptive capability of the device is attained also by the knowledge
base update law driven by the supervisory control. This knowledge
base resides in an external personal computer (PC) or notebook that
is the heart of the supervisory control, where the apparatus
computes optimization routines, and self-evaluation metrics to
establish its performance over time, to determine required
adjustments in the system set points and produce an updating law
that is fed back into the system from this higher level of
control.
[0026] The control law provided in the device allows a feedback
mechanism to be implemented based on electrical, chemical,
cognitive, intellectual, sensory and/or magnetic brain stimulation.
The main input signal to the feedback controller is the probability
of having a seizure for one or more time frames. The supervisory
control is based on an external control loop, operating at a higher
control level, that compiles new information generated at the
implantable device into the knowledge base at discrete steps and
provides set point calculations based on optimizations performed
either automatically, or semi-automatically by the doctor or
authorized individual.
[0027] The above and other novel features, objects, and advantages
of the invention will be understood by any person skilled in the
art when reference is made to the following description of the
preferred embodiments, taken in conjunction with the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0028] FIG. 1 illustrates an overview of the overall system
architecture of the present invention.
[0029] FIG. 2 illustrates an exemplary scheme of the multi-level
supervisory control of the present invention.
[0030] FIG. 3 illustrates the main stages and components of this
invention in order to achieve the approach presented for an on-line
implementation.
[0031] FIG. 4 illustrates an exemplary block diagram of the
intelligent data processing unit that is the core section of the
system and is mainly related to forecasting seizure or brain
disturbances.
[0032] FIG. 5 illustrates the processing logic for the selection of
an optimal feature vector.
[0033] FIG. 6A illustrates the effect of subtracting the focus
channel recorded with the intracranial EEG from its adjacent
intracranial EEG channel for a 4-minute segment.
[0034] FIG. 6B illustrates the same 4-minute of IEEG depicted in
FIG. 6A but without channel subtraction.
[0035] FIG. 7 illustrates the sliding observation window (gray
area) that can include one or more brain signal (IEEG) channels as
it is approaching an epileptic seizure.
[0036] FIG. 8 illustrates an exemplary scheme followed by the
low-level feedback control.
[0037] FIG. 9 illustrates a block diagram demarking the blocks
within the implantable device and each of the processing or control
blocks and the system, which in this case is the brain or the human
body.
[0038] FIG. 10 illustrates a block diagram of the control
mechanisms of the present invention.
[0039] FIG. 11 illustrates segments of intracranial EEG that are
useful to explain some terminology used throughout this
description.
[0040] FIG. 12 illustrates the classification of the features into
two types: instantaneous and historical features.
[0041] FIG. 13 illustrates the average power for both a preictal
and an interictal segment in two one-hour records of an IEEG
segment.
[0042] FIG. 14 illustrates the accumulated energy for the awake
record of a patient. Note that preictal (continuous lines) as well
as baseline records (dotted lines) are included in the plots to
emphasize the distinguishability and prediction potential of this
feature.
[0043] FIG. 15 illustrates the accumulated energy for the asleep
record of a patient.
[0044] FIG. 16 illustrates the accumulated energy trajectories of
80 one-hour records including 50 baselines and 30 preictal
segments.
[0045] FIG. 17 illustrates the fourth power indicator (FPI) over
time.
[0046] FIG. 18 illustrates the processing logic for the selection
of the sliding observation window size for maximum
distinguishability between classes.
[0047] FIG. 19 illustrates the k-factor as a function of the window
length for the weighted fractal dimension in four different
records.
[0048] FIG. 20 illustrates a nonlinear energy derived feature for a
preictal and a baseline record from another patient studied.
[0049] FIG. 21 illustrates the thresholded nonlinear energy in five
preictal/ictal one-hour segments and six one-hour baseline
segments.
[0050] FIG. 22 illustrates the location and magnitude of the short
term energy of the wavelet coefficient above the long term energy
adaptive threshold.
[0051] FIG. 23: illustrates the power in alpha band for preictal
and baseline records.
[0052] FIG. 24 illustrates an IEEG segment (top) and the spike
detector output (bottom). .
[0053] FIG. 25 illustrates the excess of the spike detector output
over a pre-established threshold over time in four preictal/ictal
and four baseline records.
[0054] FIG. 26 illustrates the absolute value of the 4.sup.th scale
wavelet coefficients average, for five seizure records from the
same patient.
[0055] FIG. 27 illustrates graphs of the mean frequency of a
seizure (top) and a baseline (bottom).
[0056] FIG. 28 illustrates how features are aligned to conform the
feature vector and how the span used is the same for features
generated with different window lengths.
[0057] FIGS. 29A-29B illustrate graphs that are proportional to the
probability density functions (pdfs) of the feature fractal
dimension for each of the classes defined in two different
patients. Note the overlap region between the classes is marked
with the cross-hatched lines.
[0058] FIGS. 30 and 31 illustrate scatter plots demonstrating the
complementarity of features for two different patients in
1-dimensional and 2-dimensional plots.
[0059] FIG. 32 illustrates an exemplary probabilistic neural
network (PNN) architecture.
DETAILED DESCRIPTION OF THE INVENTION
[0060] The preferred embodiment of the invention uses brain
electrical signals or other input signals and an implanted
processor to predict and provide early detection of the
electrographic onsets of brain events such as seizures in an
on-line intelligent arrangement that facilitates a wide variety of
options. FIG. 1 is an overview of the overall system architecture
from the data input to the output signal indicating the probability
of having a brain disturbance or seizure, and to the closed-loop
controls included in the system. The data is sketched as brain
electrical activity, but it is not restricted to this type of
activity; it can also include chemical, magnetic, temperature,
blood pressure, and/or any other physiological variable that can
contain relevant information for prediction and early detection of
the seizure onset. In FIG. 1, the main system blocks can be
visualized starting at the data generation block 100, then the
intelligent data processing unit 200 which is a key part of the
system responsible for forecasting, and the low level and high
level closed-loop controls 300 and 400, respectively that tie into
a supervisory control approach. In this figure, the data generation
block 100 does not include the brain, which is the plant in this
case; rather it only includes the electrodes, cables, and any
sensor used to capture physiological variables that go into the
forecasting section or intelligent data processing unit 200. The
system is implemented with both an off-line and on-line
methodology. The off-line part of the method plays a role at the
initialization stage, and after that, at subsequent adaptive
parameter re-tunings, setpoint readjustments, and at a higher layer
of hierarchy as a research tool seeking for an understanding of the
mechanisms that operate during epileptic seizures or brain
disturbances, and investigating new algorithms or features for
prediction and early detection of the UEO of seizures.
[0061] FIG. 2 illustrates the scheme of the multi-level control,
where the three layers of this control scheme are depicted. The
control actions are performed through these layers organized in a
hierarchical manner. The main goal of the multi-level control is to
keep the patient from having seizures despite environmental and
physiological load disturbances. To achieve this objective, a
supervisory control is implemented providing (a) continuous
regulation of the controlled variables, (b) adaptation to external
or internal changes over time, and (c) a knowledge base used to
accomplish the regulation and adaptation by incorporating
information as it arises, and updating the system settings and
parameters appropriately. At the regulatory layer, a low level
supervisory control 300 takes care of the actuators (stimulation
units) and determines and adjusts their settings in a continuous
fashion. The control in this layer is based on the implanted
processor. At the coordination layer, the high level of supervisory
control 400 is achieved, based on an external computer where the
knowledge base resides. This layer is responsible for re-tuning
system parameters such as those related to fusion of sensory data,
feature extraction, feature normalization, neural network
retraining, fuzzy logic adjustments, fault diagnosis of actuators,
sensors, implantable device, etc. This layer can operate in an
automatic mode where a master program monitors the controlled
variables and updates the control law accordingly; or in a
semi-automatic mode where the doctor or specialist can input
parameters directly into the system via the master program user
interface. At the highest level is the research layer based on
another external computer 600 whose major function is to serve as a
research tool to investigate new more powerful algorithms for
seizure or brain disturbances, UEO prediction and detection, new
control strategies, other types of parameter adjustment, and also
to analyze physiological mechanisms that can explain seizures and
other brain disturbances. This layer gathers information coming
from different patients forming a database for research and
development.
[0062] At the initialization stage, during the off-line part of the
method, the system is installed and the initial settings are
determined for all the blocks indicated in FIG. 1. The on-line
operation follows after all settings are adjusted according to the
patient. Future generations of this invention might automate the
off-line procedure, turning the apparatus into an almost completely
on-line system with the exception of the electrodes positioning,
the implantable device installation, and transference to the
implantable device of newly developed and released algorithms
(i.e., new features).
[0063] The initialization and operation of this apparatus is
divided into three stages: pre-implantation and initialization,
forecasting, and controlling. FIG. 3 provides an exemplary diagram
illustrating the fundamental blocks that manage these stages. The
stages are initiated consecutively and under different procedures.
The first stage includes the installation and manual or automatic
off-line tuning of the system. It has optional steps depending on
the particular patient requirements, on the seizure complexity, and
on whether the system is feature/parameter-tuned or only
parameter-tuned. A feature/parameter tuned device refers-to a
system where the features are selected for each patient, depending
on which features can capture the seizure UEO in advance.
Therefore, different patients have different features within the
feature vector, and once these features are selected their
parameters are tuned. A parameter-tuned system uses the same
features for all patients, and tunes the parameters of each feature
on a patient basis. One common parameter that can be adjusted for
all the features is the running window length used in the feature
extraction.
[0064] Summarizing this idea, the embodiment of this invention is
patient-tuned, with two possible alternatives. Either the same
features are used for all patients and their parameters are tuned
according to each patient, or the features are selected according
to the patient and their parameters adjusted on a patient basis as
well. The second approach is the more robust and is the system
default.
[0065] An overview of the steps that comprise the initialization
and operation of this apparatus is presented next. An exemplary
general diagram of the stages and blocks involved in each stage is
illustrated in FIG. 3.
[0066] 1. First Stage: Implantation and Initialization
[0067] The patient undergoes a surgical procedure in order to
accomplish the implantation and initialization stage. The following
steps are used as part of the implantation procedure.
[0068] Step 1: Determination of focus region for correct
installation of the implanted brain electrodes.
[0069] Step 2: Appropriate installation of the electrodes and other
sensors. The sensors can be selected from the group of (a)
intracranial electrodes; (b) epidural electrodes, such as bone
screw electrodes; (c) scalp electrodes; (d) sphenoidal electrodes;
(e) foramen ovale electrodes; (f) intravascular electrodes; (g)
chemical sensors; (h) pupil dilation sensing systems; (i) eye
movement sensors; (j) heart rate sensors; and (k) body temperature
sensors.
[0070] Step 3: Implantation of the electronic device into the
brain. Once the implantation is completed, the initialization of
the system is the next part of the implantation and initialization
stage. In one embodiment of the invention, the initialization is
performed by the implantable device in combination with an external
PC or notebook or equivalently by the regulatory and the
coordination layers, respectively. This is possible because the
system has an optional external portable module 500 that contains
an external communication unit 510, a settings adjustment unit with
display and keypad 570, an intermediate storage device 560, a
battery recharger 550, patient input channels 540, and data output
channel 540 as shown in FIG. 4. The external communication unit 510
creates a data flow path from the internal communication unit 280
such that the data acquired by the implantable device, blocks 100,
200, and 300, is transferred to the intermediate storage device 560
within the external portable module 500. In this embodiment, at the
initialization stage data must be collected to select and tune the
features appropriately according to the patient. This implies that
one or more brain disturbances or seizures must have been recorded
to carry out the parameter tuning and/or feature selection.
Therefore, the patient may walk out of the hospital with the
external portable module 500 activated, while the system is still
in the initialization stage and the forecasting has not started,
and then return later for parameter tuning and/or feature
selection. The recording time autonomy of the system depends on the
final memory capacity achieved in the intermediate storage device,
which can be based on a flash memory card that can store 160 Mbytes
or more, or on any other type of memory device suitable for this
portable module. Using a sampling rate of 200 Hz in the A/D
converters and assuming an intermediate storage device of 140
Mbytes which may evolve into a higher capacity device as the
technology advances, the portable module confers the equipment with
a two-day recording time autonomy for two channels or more as new
higher memory devices become available. This means the patient
either has to be back in the hospital or have the system connected
to an external PC at home every two days for data downloading from
the intermediate storage device into that external PC, or into a
remote PC that can be located at the doctor's office and where the
information can be loaded via the Internet. In either case, the
information is transferred onto the designated hard disk. An output
signal is triggered by the external portable module before the
intermediate storage device is full, reminding the patient that it
is time for data downloading. If the patient does not download the
data stored, then the intermediate storage device starts operating
in a first in first out (FIFO) mode, such that once the download is
accomplished only the last two days of data are available. With the
continuous improvements in technology, the time between data
downloadings can become longer as higher memory capacity devices
are developed. When four or five brain episodes are recorded and
downloaded into the high level controller, a feature selection
process can then take place in the external PC or notebook if the
feature/parameter approach is used, otherwise this step is skipped.
The implantable device is based on a microprocessor, a digital
signal processor (DSP), a field programmable gate array (FPGA), or
an application specific integrated circuit (ASIC) processor 290,
and the specific block of the implantable device that operates
during the initialization is the intelligent data processing unit
200 whose major function is forecasting the brain event or seizure
once the feature vector is established. FIG. 4 illustrates a
diagram of the intelligent data processing unit 200. The
initialization part can be split out in the following steps.
[0071] Step 4: Installation of the external portable module
500.
[0072] Step 5: Continuous data recording into the intermediate
storage device 560 and downloading into the external PC or notebook
400 until around five or more brain disturbances or seizures are
recorded. Ideally at least five brain disturbances should be
recorded, however depending on the specific case, fewer or more
brain disturbances may be required before proceeding with the next
step.
[0073] Step 6: Sensor data preprocessing and fusion followed by
feature extraction and selection at the high supervisory level in
the external PC 400 where the data has been stored after
downloading.
[0074] Step 7: Selection of the best feature set according to the
procedure sketched in FIG. 5 by the coordination layer 400. The
final product of this step is the establishment of the feature
vector. This step can be skipped when the parameter-tuning approach
is used.
[0075] Step 8: Transference and setting of the selected feature
programs into the implantable device.
[0076] In this embodiment of the invention the feature/parameter
approach is used, and therefore, the initial parameter tuning for
each of the features selected and for the other system blocks is
completed in the external PC or notebook 400. However, if the
parameter-tuning approach is used in combination with the external
portable module 500 for data recording, then either the external PC
or notebook 400 or the implantable device processor performs the
initial parameter tuning.
[0077] In another embodiment of the invention, a manual parameter
tuning is accomplished by the doctor or authorized individual
through the external portable module 500 via the settings
adjustment unit 570, based on previous knowledge information of the
patient, on historical information available from other patients,
and on the specialist experience. In other embodiments of the
invention, the initial parameter tuning is performed automatically
by new generations of the implantable device based on the
development of new devices and technology advancements.
[0078] To summarize, in the default embodiment of the invention,
the initialization part of this stage is performed by the
implantable device 200, 300 and by the external computer 400. The
core of the supervisory control that resides in the external
computer 400 located within the coordination layer can be assisted
by a doctor or specialist to establish desired setpoints, so that
the system parameters can be tuned properly for the patient.
[0079] 2. Second Stage: Forecasting
[0080] The second stage is the system core, in which the
forecasting takes place. FIG. 4 shows a block diagram of this
stage. It encompasses the on-line implementation of the forecasting
system 200, which includes components for pre-processing 210,
analog to digital conversion 225, 235, real time analog and/or
digital feature extraction or processing 245, 220, respectively,
the feature vector generator 250, the intelligent prediction
analysis/classification 260 for estimation of the probability of
having a seizure within certain time frames and alerting when a
seizure is approaching, the internal communication unit 280 and the
external portable module 500. The closed-loop feedback control that
resides in the implantable device is not activated at this point. A
description of the sequential tasks performed in this stage
follows.
[0081] Step 1: Real time pre-processing of the input signals from
different sensors. In the case of sensors capturing the brain
electrical activity, typical preprocessing includes subtracting the
focus channel signal from the adjacent channel and filtering when
necessary (FIG. 1, block 200; FIG. 4, blocks 211, 213). FIGS. 6A-6B
present the effects of adjacent channel subtraction on the IEEG
signal. FIG. 6A presents a higher quality signal since a lot of
artifacts present in FIG. 6B were abated by the subtraction. This
is done to remove any noise common to both channels. As a result,
any common mode cortically generated signals are also eliminated.
However, this is not felt to affect adversely the seizure onset
forecasting, since the seizure onset patterns are highly localized
to the focus channel. IEEG data have been processed both with and
without channel subtraction. Results by Esteller et al. ("Fractal
dimension characterizes seizure onset in epileptic patients",
ICASSP 1999) have demonstrated better detection and forecasting
with channel subtraction for specific features. This shows that for
those particular features the spatial separation between the
electrodes inside the brain is short enough to cancel the common
noise in that region, and long enough to capture a voltage
difference between the focus and its adjacent electrode. Of note,
each of these electrodes records the global activity of many
thousands of neurons.
[0082] Step 2: Depending on the type of processing required by each
particular feature, they are extracted either at an analog level
(level I or 220) or at a digital level (level II or 245), whichever
is more suitable for the specific feature considering computational
requirements, hardware capacity, and time constraints. The analog
level of feature extraction is indicated in block 220 of FIG.
4.
[0083] Step 3: Digitizing 225, 235 and recording 230, 240, 270 the
preprocessed and processed sensor signals with optional downloading
of the recorded data into the computer 400 or into the intermediate
storage device 560.
[0084] Step 4: Extraction of the features at the digital level as
indicated in block 245 of FIG. 4.
[0085] Step 5: Generation of the feature vector or feature vectors
250 if more than one time frame is used. Features extracted at
levels I and II are combined following a running-window
methodology. This methodology is utilized for the generation of the
feature vector(s) as sketched in FIG. 7. For a pre-established
window length, the features within the feature vector are computed.
Subsequently, the window is shifted over the input signal or
signals allowing some overlap and the feature is computed again.
The feature sampling period is given by the shifting for which
reasonable values are around half a second.
[0086] Step 6: The intelligent prediction analysis/classification
can have an additional processor if the need arises and the
processing time of the central processor 310 is not sufficient for
the computations required by the implantable device. Before
describing the intelligent prediction analysis/classification step
260, a feature normalization step is necessary. Typically the
normalization involves subtracting the mean and dividing by the
standard deviation. This is performed directly by the feature
vector generator 250. Logically, the feature mean and standard
deviation have to be estimated. The estimation of these parameters
is conducted through a longer time window, which implies that a
succession of feature vectors has to be generated and stored to
estimate the values for these parameters. This procedure is
performed by the implantable device, and more specifically by the
central processor 310 or the additional processor if this is
available. Once the parameters have been determined, the features
are normalized appropriately. The parameters are updated as new
feature values are computed in an on-line mode of operation,
providing adaptability at this inner layer of the system. These
parameters are also estimated by the high level supervisory control
400.
[0087] Step 7: Intelligent analysis of the feature vector, for each
time frame considered, is performed through a fuzzy system or a
neural network (NN) such as the probabilistic NN, the k-nearest
neighbor, the wavelet NN or any combination of these, to provide an
estimation of the probability of having a seizure for one or more
time frames. This analysis is performed by the block denoted as
intelligent prediction analysis/classification 260 illustrated in
FIGS. 1, 4 and 8. The implanted processor 310 guides this analysis,
however if an additional processor is used, this will take the
leadership for this block. An in-depth presentation on how the
probability of having a seizure is estimated can be found in the
co-pending patent application Ser. No. 09/693423. The coordination
layer of the supervisory control 400 must be connected periodically
or as required or indicated by the doctor through the external
portable module 500 with the goal of re-tuning the system
parameters or adjusting the set points according to physiological
and environmental changes. It is expected that as time progresses
the actions required from the supervisory control will lessen, and
therefore, the external connection to a PC, for further analysis
and inspection of the system or for data recording may be needed
rarely or occasionally. The ideal scenario is that the system
reaches a steady-state equilibrium where brain episodes are
prevented by the brain stimulations such that they do not occur at
all, and a clear measure of this is given by the seizure frequency
of the patient. Thus, a combination of this adaptive implantable
device with a complex system like the brain should exhibit zero or
very near zero seizure frequency to consider that it has reached
the ideal equilibrium.
[0088] Step 8: The probability output of having a seizure for one
or more time frames is shown on a portable display 520 contained
within the external portable module 500. When this probability is
higher than an adaptive threshold, a sound, visual, and/or tactile
alarm(s) is(are) activated to alert the patient of the oncoming
seizure. A more detailed description of this probability output and
its operation is presented in the co-pending patent application
Ser. No. 09/693423.
[0089] Step 9: This step utilizes the external portable module 500
and the internal and external communication units 280, 510,
respectively). The external portable module 500 has its own
preprogrammed processor with specific tasks that include scheduling
and control of data downloading into the intermediate storage
device, data transference from the intermediate storage device to
an external PC with the option of transference through the
Internet, battery recharger, display and keypad, patient input
channels, output channel with the alarm(s) that indicate the
probability of having a seizure, external programming control or
settings adjustment unit 570 whose function is the programming of
the different options that the apparatus offers via the keypad, and
data transference from the external PC to the external portable
module to establish the supervisory control actions and communicate
them to the implantable device. The settings adjustment unit 570 is
password-activated such that it is protected and only authorized
personnel can access it.
[0090] Step 10: The communication link is accomplished by a direct
electrical connection, by telemetry, by magnetic induction, by
optical or ultrasound connection as indicated in FIG. 4. In either
case, internal and external bi-directional communication units 280,
510, respectively are used to manage the information transference
between the central processor 310 within the implantable device and
the external portable module 500. The implantable device and the
external portable module processors can write or read the internal
and external communication units 280, 510, respectively, any time
that it is necessary. Every time the internal 280 or the external
communication unit 510 receives information from the other end, it
sends an interrupt to the processor within the implantable device
or within the external portable module, respectively. Interrupt
priorities are assigned according to the importance of the
information transmitted.
[0091] Step 11: The system records input signals in several
possible modalities. One modality records the physiological input
signals during approximately one hour or more depending on the
on-board memory capability 270 finally achieved in the implantable
device. In this modality the recording starts some time before the
probability threshold for approaching seizures is reached, by
utilizing a set of buffers available for the task of temporarily
storing the data. This modality is permanently activated and
provides information to the internal adaptation loop of the low
level controller when it is activated. A second modality utilizes
the external portable module 500 and is activated upon connection
of the module to the system. It has the option of recording
continuously the input signals, the feature vector, and/or the
controlled variables into the intermediate storage device 560 via
the communication link. Depending on the data option selected, the
recording time autonomy will change. It will be the longest when
only the controlled variables are recorded, and the shortest when
the input signals, the features, and the controlled variables are
selected for recording. The external portable module 500 indicates
when the intermediate storage device requires downloading of its
stored data into an external PC representing the third storage
modality. These downloading times are required to keep memory
available in the intermediate storage device for incoming data.
Three levels of data downloading are possible, one from the
implantable device 200, 300 to the external portable device 500,
and the others from the external portable device 500 to the
external PC 400. The communication link for the first level of data
downloading from the implantable device into the intermediate
storage device is established by either a telemetry unit, a special
hook up, magnetic induction, ultrasound or optical connection. The
third storage modality has two options or levels of data
downloading. One level of data downloading from the intermediate
storage device to the external PC is established by a direct
electrical connection in the form of a USB port, a serial port, or
a parallel port. The information downloaded into the external PC is
stored on a hard disk specific for this purpose. The second level
of data downloading from the intermediate storage device to the
external PC is accomplished through the Internet. In this form the
information can be downloaded into a computer that can be at a
different physical location, either at the doctor's office,
laboratory, etc. The information recorded on that disk can be
retrieved by the supervisory control at the coordination layer. At
the automatic level of operation of the supervisory control, the
information is retrieved by an intelligent master program that is
running in the background; and at the semiautomatic level of
operation, the information is retrieved by the doctor, the patient,
or an authorized individual, via the software user interface that
allows the interaction with the master program. Any of these
recording modalities can be manually deactivated by the doctor or
an authorized individual.
[0092] Step 12: Before proceeding with the activation of the
implanted close-loop control (i.e., the starting step of the next
stage), an adaptation time must be allowed for the forecasting
block to reach a finer tuning. The time required for this initial
adaptation procedure highly depends on the seizure frequency of the
patient. At least five to ten seizures must have occurred after the
forecasting is activated to warrant proper adjustment of this
stage. The adaptation requires the use of the external portable
module 500 for data recording and communication with the
supervisory control. The initial adaptation is performed at
periodically discrete times when the patient connects the external
portable module 500 to the high level supervisory control 400,
either as a direct connection to the computer where the master
supervisory program that manages the high level control resides, or
to another external device or computer that will transmit and
receive information to and from the supervisory control computer
via the Internet. The initial time spans between consecutive
communications with the supervisory control may be around two days.
After this initial adaptation/learning procedure the system can
start the third stage or controlling stage, where the implantable
close-loop control is activated. The adaptation will continue but
at longer time spans that can be linked to a doctor or a specialist
check-up appointment where the supervisory control re-tunes
setpoints and readjusts parameters according to the most recent
information archived in the knowledge base. Occasionally, the
doctor or specialist can request at his discretion that the patient
stores the data into the supervisory control at the coordination
layer continuously for a week or the time they considered, or only
at the specific times brain events or seizures occur, in which
case, the patient is permanently wearing the external portable
module, but he only downloads the data when a brain disturbance
occurs, either a seizure, an aura, or any other brain event. In
this form, the brain event and two days of consecutive data before
the event occurred are stored in the intermediate storage device.
This allows the master program and/or the specialist to reexamine
the scenario, to consider new variables not observed previously,
and to re-tune the system in a similar way that a car tune-up is
conducted. This adaptation ability accounts for long-term
physiological changes and for environmental changes, which assures
the long lasting capacity of the apparatus. Furthermore, the
highest layer (research layer) 600 allows the specialist to conduct
innovative research and explore new horizons regarding brain events
that can provide new evidence to explain the mechanisms that
operate during these disturbances and brain diseases. In other
words, this invention also acts as a research tool for the
particular brain events that are being forecasted, without
modifications to the apparatus or additional burden to the
patient.
[0093] 3. Third Stage: Controlling
[0094] The third stage is basically concerned with the control part
of the system. It comprises a multi-level control illustrated in
FIG. 2, that includes a regulatory (low level) control, a
coordinating (high level) control, and a research (development
level) layer from which modifications to the control laws in the
lower layers can be derived. The high level control is provided by
the supervisory control at the coordination layer that operates in
two levels, i.e., an automatic and a semiautomatic level. The low
level control is provided by a supervisory-regulatory control 300
that resides within the implantable device and whose main tasks are
the internal parameter adjustments or tuning 320, and the brain
feedback stimulation 330, 340 to avoid or mitigate seizures. The
brain feedback stimulation is provided by the stimulation unit 340
shown in FIG. 8. In this figure, the outputs of the stimulation
unit 340 (electrical, magnetic, chemical, sensorial or cognitive
stimulation variables) are directly fed back into the brain,
altering the net brain activity and becoming the manipulated
variables 341-345. These manipulated variables are adjusted
dynamically to keep the controlled variables at their set points or
below the set points. The controlled or output variables, which
quantify the performance or quality of the final product are the
probability of having a seizure in one or more time frames and the
overall system performance metric. The probability of having a
seizure can be a vector if more than one time frame is used to
estimate this probability. The stimulation block 340 can be
manually deactivated by the doctor or an authorized individual.
When this block is deactivated, the apparatus becomes a pure
forecasting/warning device, which is the state it has at
initialization. Two levels of stimulation are available in the
stimulation block 340 depending on whether the control action or
manipulated signal is activated by the patient or by the device.
Stimulations at the patient level include sensory/perceptive and
cognitive stimulations, and at the device level include electrical,
chemical, magnetic, and certain types of sensory stimulation. This
stage comprises the following steps.
[0095] Step 1: The low level supervisory control or implanted
closed-loop control 300 is activated manually from the external
portable module 500 or automatically via the high level supervisory
control 400 through the external portable module.
[0096] Step 2: The controlled variables given by the probability of
having a seizure for one or more time frames and the overall system
performance metric are used as control feedback signals by the low
level controller to prevent seizures by producing an intermittent
electrical, chemical and/or magnetic stimulation 341-343, or by
instructing the patient to go into a previously specified sensory
or cognitive procedure 344, 345. The duration, magnitude, type, and
frequency of the electrical, chemical, or magnetic stimulation is
adjusted to maintain the controlled variables at their set-points
or range-points, as well as the duration, intensity, and type of
sensory or cognitive stimulation. Prediction times on the order of
minutes to an hour can be obtained with this invention (see FIGS.
15-17, 25-26), and in the worst cases on the order of seconds
(FIGS. 20). This represents ample time to avoid a seizure by
releasing small quantities of a drug (chemical stimulation), by
electrically stimulating focal points to ward off synchronized
nerve impulses, by wearing a special helmet that provides a
magnetic stimulation, by solving high cognitive problems, or by
experimenting with sensory stimulation such as music, flavors,
images, tactile sensations, or odors. The intensity as well as the
level of invasiveness of the stimulus gradually increases with the
probability of having a seizure. This multi-therapeutic approach is
described in more detail in the co-pending patent application Ser.
No. 09/693423. However, a description of several invasive
intervention measures is also described herein.
[0097] The intelligence structure of this invention is coupled to
an array of interventions based upon electrical stimulation,
chemical infusion and synthesis of artificial neuronal signals to
counteract developing seizures as precursors build over time. The
intensity of intervention, modality of therapy and spatial
distribution of therapy are all adjusted as the probability of
seizures increases over time. A guiding principle of these
interventions is that the most benign forms of therapy are
initiated relatively early in seizure generation and over a
relatively small region of the brain, so as to cause little or
minimal disruption of normal activity when the probability of
seizure onset is relatively low. This will allow intervention to be
triggered by prediction thresholds with high sensitivity (e.g.,
very low false negative rate) at the cost of a relatively low
specificity (e.g., relatively high false positive rate). As the
probability of seizures increases, therapeutic stimuli are
increased in intensity, duration, frequency of delivery, and are
delivered over a wider area of the brain. Since patterns of seizure
precursors and their spread in space and time leading up to
seizures are mapped and used to train the device on each individual
patient, therapy is delivered over broader areas, just ahead of the
anticipated region of spread, as seizure precursors develop; if
they do not respond to earlier treatment. In this scheme, therapy
can be delivered locally, in the region of onset, in a distribution
surrounding the region of onset, isolating it from recruiting
adjacent regions of the brain and spreading. Therapy can also be
delivered locally and/or remotely in subcortical regions such as
the thalamus, basal ganglia, or other deep nuclei and regions,
escalating in intensity, type of stimulus and distribution of
action, as seizures progress. This same principle is applied to
therapeutic intervention if electrical seizure onset takes place,
effecting treatment in the general region of onset, in deep brain
structures which modulate the behavior of the seizure focus, or
both simultaneously.
[0098] Interventions can include the following: (1) rhythmic
electrical pacing, which changes in frequency, intensity and
distribution as the probability of seizure onset reaches a
threshold and increases; (2) chaos control pacing; (3) random
electrical stimulation to interfere with developing coherence in
activity in the region of and surrounding the epileptic focus; and
(4) depolarization or hyperpolarization stimuli to silence or
suppress activity in actively discharging regions or regions at
risk for seizure spread. This activity can also be delivered to
numerous electrode sites to create a type of "surround inhibition"
to prevent progression of seizure precursors. These stimuli can
also be delivered sequentially in a "wave" that sweeps over a
region of tissue, so as to progressively inhibit normal or
pathological neuronal function in a given region(s) or tissue,
including cortical and subcortical regions.
[0099] The principle of altering and developing therapy in response
to the changing probability of seizure, and/or the detection of
specific events in seizure evolution, including electrical seizure
onset and spread, is also applied to the delivery of chemical
therapy. In this fashion, active therapeutic agents are infused or
otherwise released in the brain regions where seizures are
generated, or to where seizures may spread. As seizures become more
likely, the amount, concentration or spatial distribution through
which a chemical agent is delivered are all increased. As with
electrical or other therapeutic interventions, patterns of delivery
can include infusing a drug directly in the epileptic focus, in an
area surrounding it, or to regions involved in early spread, or to
more central or deep brain regions, which may modulate seizure
propagation. These same therapeutic principles apply to
distribution of maximal therapy when electrical seizure onset is
detected, including distributing therapy to regions where seizures
are known to spread and propagate. Last-minute treatment may
include release of larger amounts of drug into the cerebrospinal
fluid (CSF) space for circulation over wide regions of the brain or
into the cerebral circulation. Other types of pharmacological
agents may also be used in this scheme, such as agents which are
activated by oxidative stress, which may themselves increase the
concentration and distribution of an active therapeutic agent as
seizure precursors evolve and the probability of seizures
increases.
[0100] Therapy may also include delivery of stimuli, electrical,
chemical or other, to peripheral or central nerves or blood
vessels, in a graded fashion, as the probability of seizures
increases, building up to therapy of maximal intensity at the
detection of electrical seizure onset. Therapy may also include
sensory stimulation (touch, temperature, visual, auditory
etc.).
[0101] Finally, therapy may consist of synthesized, artificial
neuronal signals delivered in such a way as to disrupt
electrochemical traffic on the appropriate neuronal networks
including or communicating with the ictal onset zone. Examples of
such interventions might include transmission of synthesized
signals which increase the output of specific cell populations,
such as inhibitory interneurons, specific nuclear regions in the
thalamus or other deep structures.
[0102] Using any or all of these methods singly, or in combination,
therapy is directed toward preventing seizure onset, or isolating
the development of seizures and their propagation so as to prevent
or minimize clinical symptoms and the impact of these events.
[0103] Step 3: An evaluation is accomplished by the intelligent
prediction analysis/classification block 260 within the intelligent
data processing unit 200, to estimate the prediction performance,
by measuring when possible, key parameters such as prediction time
frame threshold error (PTFTE), false negatives (FNs), false
positives (FPs), average prediction time achieved (APTA), seizure
duration (D.sub.Sz), etc. The PTFTE is directly quantified from the
number of FPs and FNs. It can be measured only when either the
controlling block 300 is deactivated (no low level control/no
stimulation), or when it completely fails due to a general system
failure, which implies that no electrical, chemical, magnetic,
sensory, or cognitive stimulation is performed. When the
stimulating system is deactivated, the apparatus is used for
forecasting and not for controlling seizures. The prediction time
frame threshold is the adaptive probability threshold used to
declare an oncoming seizure for a particular time frame. In order
to quantify a fault in the prediction time frame threshold, a
measure of the achieved prediction time is needed, and therefore,
the seizure UEO detection is required. The achieved prediction time
is measured as the elapsed time between the moment the adaptive
probability threshold that declares a seizure or brain disturbance
is reached and the moment the UEO detection occurs. Among the
several errors typically committed in this type of measurement, the
biggest error in the achieved prediction time is due to the error
in the UEO detection, but this error is within the range of
seconds. Fortunately, the seizure UEO detection does not entail any
additional circuitry or programming, since the prediction
algorithms used to compute the feature vector also have the
capability of seizure onset detection. The effects sensed and
monitored through the selected features typically exhibit a more
drastic variation as the seizure approaches, reaching their maximum
change during the ictal period near to the UEO. This is logical and
experiments conducted have proven that in most cases, the feature
vector can be used efficiently for seizure prediction as well as
seizure detection ("Accumulated Energy Is a State-Dependent
Predictor of Seizures in Mesial Temporal Lobe Epilepsy,"
Proceedings of American Epilepsy Society, 1999, and "Fractal
dimension characterizes seizure onset in epileptic patients," IEEE
Int. Conf. on Acoustics, Speech, & Signal Proc., 1999). The
probability of having a seizure is a continuously changing function
of the time and the time frame under consideration P.sub.TF(Sz,t).
If for a particular time frame (TF) considered, the probability of
having a seizure P.sub.TF(Sz,t) reaches the adaptive probability
threshold value P.sub.o that declares an approaching seizure, then
a false positive (FP) is declared when a time identical to the TF
under consideration has elapsed and no seizure has occurred,
provided that the low level control is deactivated, and
disregarding if there are oscillations of P.sub.TF(Sz,t) around
P.sub.o. Even if P.sub.TF(Sz,t) for that TF goes above the
threshold and right immediately goes below, a FP must still be
quantified. If P.sub.TF(Sz,t) is above the threshold during time
T.sub.up longer than TF, then the number of consecutive and
non-overlapping segments of TF duration that fits into T.sub.up+TF
is equivalent to the total number of FPs that should be quantified
for that TF. Note that rather than fitting these consecutive and
non-overlapping segments of TF duration into T.sub.up, they are
fitted into T.sub.up+TF because the FPs are measured into this
prediction framework such that the longer time P.sub.TF(Sz,t) is
above P.sub.o without a seizure occurrence, the more FPs must be
quantified. One FP is defined in the ideal case, when
P.sub.TF(Sz,t) is above P.sub.o for an instant at time t.sub.o,
which mathematically will be described as a
P.sub.TF(Sz,t)=.alpha..delta.(t-t.sub.o), where .delta.(t-t.sub.o)
is a delta function at time t.sub.o and .alpha..gtoreq.P.sub.o; in
this case, one FP is quantified. If
P.sub.TF(Sz,t)=.alpha..PI.(t-t.sub.o,t-t.sub.o-T- .sub.up),
indicating that P.sub.TF(Sz,t) is a pulse of amplitude .alpha.,
such that .alpha..gtoreq.P.sub.o, and duration T.sub.up, such that
T.sub.up=1.25 TF then the number of FPs is quantified as 2.25.
Considering the usual definition of a FP, it should be an integer
number; however, the definition provided in this invention
penalizes this type of error with more accuracy. Otherwise,
T.sub.up=1.25 TF and T.sub.up=0.65 TF would yield the same integer
number of FPs. If P.sub.TF(Sz,t) is again a pulse as mathematically
described earlier, with amplitude .alpha., such that
.alpha..gtoreq.P.sub.o, and duration T.sub.up, such that
T.sub.up=1.25 TF, but this time a seizure indeed occurred at time
t=t.sub.o+t.sub.1 such that t.sub.o+t.sub.1=1.1 TF, then one FP has
to be quantified even though the seizure occurred, because from the
beginning of the pulse until time TF no seizure had occurred. FPs
are quantified only when the controlling block is deactivated;
otherwise, the activated control produces a stimulation to avoid
the seizures or brain disturbances and the FPs will be unnoticed
since they will be confused with avoided seizures. The FNs are
quantified in three different ways. The first way occurs when the
achieved prediction time as defined earlier is zero or less than
one tenth of the time frame TF/10 for which P.sub.o is activated.
The second way occurs when P.sub.TF(Sz,t)<P.sub.o, but a seizure
occurrence is indicated by the patient through the patient input
channel via the external portable module. The third way occurs when
the supervisory control at the semiautomatic level indicates a
seizure occurrence from direct inspection of the stored data by a
specialist or doctor. The false negatives (FNs) are quantified over
time to determine the prediction performance.
[0104] Step 4: The overall system performance metric is computed
from the prediction performance and from the prevention
performance. Along with the prediction performance, a prevention
performance is determined by counting and storing the number of
prediction-stimulations that were performed but failed to stop a
seizure with respect to the total number of
prediction-stimulations. This provides an indication of the failure
and success rates of the stimulation block (lower level control)
340. In addition, the seizure frequency over time, the average
seizure duration over time, the "aura" frequency over time, etc.
are used to quantify the prevention performance. This is an
important statistic since a reduction in the patient frequency of
seizures after the device is implanted determines the apparatus
performance. The overall apparatus performance is quantified in a
metric that is a linear or a nonlinear combination of at least one
of the performance measures assessed and is used in combination
with the probability of having a seizure as feedback control
signals. Also the system can utilize each of the measures that are
used to compute the overall system performance (FPs if the
stimulation unit is deactivated, FNs, patient seizure frequency,
aura frequency, prediction-stimulation failures, total number of
prediction-stimulations, D.sub.Sz, APTA, etc.), or the prediction
performance and the prevention performance as a feedback vector,
rather than using the overall apparatus performance directly.
[0105] Step 5: The stimulation block 330 and 340, contained in the
low level controller 300 receives as input, the control feedback
signals or probability of having a seizure within one or more
chosen time frames produced in the forecasting section as well as
the different measures used to compute the prediction and
prevention performances. The information contained in this feedback
vector is used to adjust each of the stimulation block 340
parameters (intensity, duration, and frequency) and to determine
the start time and the type of stimulation depending on the patient
and on the seizure probability time frame activated and the
probability value itself, and the type of stimulation within that
kind, i.e., if a sensory stimulation of a visual kind is used, the
types can be relaxing movie or picture, funny movie or picture,
scary movie or picture, suspense, etc. Similarly, for each of the
kinds of stimulations available 341-345. Note that the
sensory/perceptive and cognitive kinds of stimulations have
sub-kinds such as visual, auditory, tactile, smell, and taste,
within the first category or kind; and reading, mathematical
computation, and logic reasoning problems, within the cognitive
kind.
[0106] Step 6: Initially, the feedback control law and the
knowledge base update law are determined as a basic linear
relationship between the variables that are fed back and the
parameters that need to be adjusted according to the desired goal
of a seizure-free patient with minimum invasion. Through the
subsequent on-line tunings the parameters within the control laws,
as well as the control laws themselves, will be updated as time
progresses. Using intuition, logic, and previous available
knowledge, mild interventions will be used first for longer TF. As
the TF activated becomes smaller and/or the mild interventions do
not decrease the probability of seizure, stronger
interventions/stimulations have to be used. Mild interventions are
the non-invasive kinds such as cognitive or sensory/perceptive
stimulations. The duration of the mild stimulation or intervention
D.sub.st, will initially be proportional to the weighted average of
the probabilities of having a seizure for each TF, where the
weighting factor in each case is given by a stimulus factor.
Mathematically, D.sub.st can be expressed as 1 D st = 1 N TF TF k
st , TF p TF ( Sz , t ) / TF ,
[0107] where NTF is the number of TFs utilized in the probability
vector, and k.sub.st,TF is a specific stimulus factor initially
determined as a function of previous available information such as
the frequency of seizures, frequency of auras (if available),
seizure duration, and type of seizure. Note that k.sub.st,TF
depends on the TF and on the kind and type of stimulus used (st).
Once the on-line operation is started and the controlling section
is activated, this specific stimulus factor is updated using FNs,
updated frequency of seizures, updated frequency of auras (if
available), prediction-stimulation failures, total number of
prediction-stimulations, D.sub.Sz achieved, APTA. The number of
stimulation kinds available depends on the patient's evolution,
initially all the stimulations proposed are used, but the
adaptation procedure at all the control layers will progressively
reduce and withdraw those stimulations with a high rate of failure.
If more than one kind of stimulation is maintained, simultaneous
stimulations can be applied according to the co-pending patent
application Ser. No. 09/693423. For stronger or invasive
stimulations, a similar control law is used initially for each of
the parameters required. For example, the electrical stimulation
requires five parameters to be assessed. The intensity and duration
are determined using the same expression for the duration of a mild
intervention, the difference is in the specific stimulus factor
that changes in each case. The other parameters are starting
stimulation time, type of electrical wave to apply, and frequency
(if there is a frequency associated with the type of waveform). The
type of waveform is initially decided as a basic waveform that is
easily generated and preferably with discrete values. In most
cases, a pulse or half period of a square wave is used as the
initial shape, but as the system gathers information from the
patient, other waveforms can be tested if results are not
satisfactory with the initial waveform. A similar criteria applies
for the frequency of the waveform, initiating the control with a
half wave per chosen duration. The starting stimulation time is
determined by the time an adaptive probability threshold is reach
by the actual probability of having a seizure for each specific TF.
Each TF adaptive probability threshold is specific for each
stimulus and is a function of the FNs, updated frequency of
seizures, updated frequency of auras (if available),
prediction-stimulation failures, total number of
prediction-stimulations, D.sub.Sz achieved, type of seizure, and
APTA.
[0108] Step 7: Relying on the research and coordination layers of
the supervisory control 600 and 400 respectively, it is expected
that the control laws will adapt to internal and external changes
and evolve over time to accomplish the desired optimal equilibrium
point where the seizure frequency reaches zero with less invasive
and minimal stimulation, such as sensory/perceptive and cognitive.
However, there are still many obscure issues regarding how the
stimulations influence the patient. As the research and
coordination layers (FIG. 2) update the incoming information, the
interaction of the doctor, specialist and/or scientist with these
two layers progresses, and the development level 600 (FIG. 2)
provides enhanced control schemes to the lower layers, the
equipment performance is enhanced over time.
[0109] Step 8: Subsequent adaptive tunings of the internal system
feature parameters, additional features (in case they are
available), and analysis/classification parameters are performed in
this step, based on the combined information of the control
feedback signal and the overall performance measures achieved by
the system (FIGS. 8, 9, and 10).
[0110] Step 9: The device has the option of reading information
introduced by the patient by using the external portable module via
the communication link shown in FIG. 4. The patient input channels
540 can be activated via the keypad, allowing the entrance of
important patient information through different channels designated
for each specific task. When information supplied by the patient is
available, it is incorporated as an additional feature into the
feature vector. In this form, the patient can provide additional
information to the system through these channels. When he feels an
aura he can press a button; when he or an individual observing him
considers that a seizure is occurring, another button or
combination of buttons can be pressed. The patient input channels
540 can be activated or deactivated directly in the external
portable module 500, as well as many other options that the system
offers.
[0111] Step 10: When the input channel of the external portable
module 500 that provides the information regarding the patient aura
sensation is activated, the system automatically adjusts itself to
consider the new available information for the seizure probability
assessment, according to pre-programmed parameters adjusted to each
individual patient automatically by the control feedback signals,
or manually by the doctor or expert.
[0112] Step 11: If the channel of the external communication unit
510 receiving the information regarding the occurrence of a seizure
is activated, then this information is used in conjunction with the
preictal and ictal data recorded to evaluate the system prediction
performance. Among others the false positives, false negatives, and
prediction times are used to assess the system performance.
[0113] Step 12: The system performance evaluation is always an
option that can be activated by an authorized person. Two different
system performance evaluations are accomplished automatically. One
at the regulatory feedback control level and the other at the
supervisory control level.
[0114] Another embodiment of the invention includes using other
input signals in the system such as blood pressure, heart rate,
body temperature, level of certain chemical substances in important
organs, dilation of pupils, eye movements, and other significant
physiological measures.
[0115] System Processing
[0116] The present invention delineates a patient-specific
systematic approach for seizure prediction or early detection of
UEO. The methodology followed is a typical approach used in
artificial intelligence and pattern recognition. But in this
invention, these methods are applied to the computational
neuroscience field with adaptations to the specific conditions of
the brain event or seizure prediction/detection problem, the
detection as a consequence of the prediction and for performance
evaluation purposes.
[0117] FIG. 1 depicts the architecture on which this invention is
based. As can be observed in this figure, once the data is
generated, a preprocessing stage is required to reduce the noise
and enhance the signal for better class discrimination with minimum
distortion and for appropriate data fusion. The preprocessed and
fused data goes into the processing block, where the feature
extraction and selection is performed. After appropriate features
have been extracted and selected (optimized), an intelligent tool
such as a neural network, fuzzy logic, or a combination of both
achieves the intelligent prediction classification/analysis.
Following this, a closed-loop control is activated and driven by
the probability of having a seizure and by the overall system
performance measures.
[0118] In prediction/detection problems the feature extraction and
selection is considered to be the key aspect necessary to achieve a
correct classification and usually is the most critical. The
intelligent prediction analysis/classification possesses a general
and well defined operation once an effective set of features is
found (see co-pending application Ser. No. 09/693423), but there is
no straightforward procedure for determining the best set of
features. However, FIG. 5 presents a flow chart with the procedure
used in this invention for the selection of the best-feature
vector.
[0119] Feature Extraction
[0120] The feature extraction is performed through a running window
method, as illustrated in FIG. 7. The shaded area is the sliding
observation window, which moves through the data as the features
are computed. The data points inside this sliding window are used
for feature generation as the window moves through the data.
Therefore, this observation window is continually collapsed into a
feature vector by means of formulas and algorithms that take
preprocessed and fused input signals and produce scalar quantities
as outputs, which then become the components of the feature
vector.
[0121] A feature library consisting of a large set of candidate
features has been developed for feature extraction and selection.
When following the feature parameter-tuned approach, an initial
pre-selection of the features to be extracted is performed, guided
by a combination of knowledge characteristics, intuition, and
brainstorming. Once a large group of features is pre-selected, the
features are computed. Two levels of features are defined at this
point: instantaneous features and historical features, which are
sketched in FIG. 12. The instantaneous or historical features can
be limited to the focus region or can be derived, as a spatial
feature arising from the combination of different regions within
the brain, and not restricted to the focal area.
[0122] Instantaneous features are computed directly from the
preprocessed and fused input signals through a running observation
window. Historical features are "features of features" that require
a second level of feature extraction, which entails the historical
evolution of features through time. From this large set of
instantaneous and historical features that are extracted (i.e.,
candidate features), the feature selection takes place.
[0123] The feature library developed contains more than 20
features. It includes a collection of custom routines to compute
the features. Features from different areas or domains are
extracted to explore a wide spectrum of possibilities. Among the
domains analyzed are time, frequency, wavelet, fractal geometry,
stochastic analysis, statistics, information theory, etc. In the
following, a description of the algorithms, assumptions, and
mathematical formulation for determining these features is
presented in combination with some of the results.
[0124] Time Domain Features
[0125] The power, power derivative, fourth-power indicator (FPI),
and accumulated energy (AE) are amplitude-based features. The
nonlinear energy, thresholded nonlinear energy and duration of the
thresholded nonlinear energy are based on an AM-FM demodulation
idea first introduced by P. Maragos, et al. ("On Amplitude and
Frequency Demodulation Using Energy Operators", IEEE Trans. on
Signal Processing, vol. 41, No. 4, pp. 1532-50). Their calculations
are provided below.
[0126] Average Power or Moving Average Power
[0127] Let the sequence x(n) be a preprocessed and fused input
signal, then the instantaneous power of x(n) is given by
x.sup.2(n). Considering that a sliding window is used, the power of
the signal becomes the average power over the window mathematically
defined as, 2 P [ n ] = 1 N 1 i = ( n - 1 ) N 1 + 1 nN 1 x ( i ) 2
,
[0128] where:
[0129] N.sub.1 is the size of the sliding window expressed in
number of points, and
[0130] n is the set 1,2,3, . . .
[0131] The moving average of the power defined above is with zero
overlap. If an overlap of D points is allowed, then the average
power becomes: 3 P D [ n ] = 1 N 1 i = 1 + ( n - 1 ) ( N 1 - D ) n
( N 1 - D ) + D x ( i ) 2 ,
[0132] where:
[0133] P.sub.D is the average power or moving average of the power
with D points of overlap.
[0134] FIG. 13 illustrates the average power for one seizure record
from an epileptic patient. Similar results were found in another
patients. This feature was obtained using a window length of 1.25
sec. or equivalently 250 points with an overlap of 0.45 sec. (90
points); however, these parameters can be changed or adjusted to
the patient.
[0135] Derivative of Power
[0136] The subtraction of consecutive samples of P.sub.D (n)
corresponds to a discrete derivative of the average power, which
can be expressed as
.DELTA.P[n]=P.sub.D[n]-P.sub.D[n-1].
[0137] Accumulated Energy (AE)
[0138] The AE contains historical information and represents a
discrete integral of the power moving average over time. From the
power records obtained from the expression for P.sub.D[n], a new
moving average window of N.sub.2=10 points or any other value
determined to be suitable for the particular patient, is slid
through the power record with a 50% overlap or equivalently Da=5
points, and a new sequence is derived as the cumulative sum of
these values. The following equation summarizes the mathematical
computation of the accumulated energy or integral of the power for
the specified band of time: 4 AE [ k ] = 1 N 2 [ j = 1 + ( k - 1 )
( N 2 - D a ) k ( N 2 - D a ) + D a P D [ j ] ] + AE [ k - 1 ]
.
[0139] This feature shows promising results for seizure prediction
of UEO, as can be seen from FIGS. 14, 15, and 16. These figures
present the accumulated energies for several one-hour records of
IEEG as if they had occurred at the same time (same time axis), but
this is just a way to compare the behavior of one-hour baseline and
pre-seizure records from different time moments. Note that the time
labeled zero corresponds to the UEO and the horizontal scale is in
minutes. FIG. 14 illustrates the AE trajectories for all the awake
IEEG records from an epileptic patient. The continuous lines of
higher final amplitude correspond to seizure records, and the
dotted lines of lower ending amplitude correspond to baseline
records. A clear separability between the seizure and baselines
records is observed from around 18 minutes before the UEO in most
of the records. FIG. 15 shows the AE trajectories after a
normalization. The one-hour IEEG segments in this figure correspond
again to seizure and baseline records, but this time from both
states awake and asleep. The normalization performed on the AE
trajectories allows comparison of awake and asleep records within
the same reference. Again in this figure the preictal segments
exhibit higher AE than the baseline segments. Except for the lowest
amplitude AE seizure record, a clear separation can be noticed
around 20 minutes before the UEO. FIG. 16 illustrates the
normalized AE trajectories for 80 one-hour segments from five
different patients. It is clear from this figure that the seizure
AE trajectories are concentrated at the top of the baseline AE
trajectories. The observed behavior is similar in other patients.
The normalization factor used over the AE was tuned for each
patient according to an off-line procedure. The magnitudes of the
non-normalized AE trajectories were always higher in asleep records
than in awake records, and also changed from one patient to
another. However, after the normalization, the AE trajectories
became within the same range of values, preserving the relative
differences within each patient.
[0140] Fourth-Power Indicator
[0141] The fourth power of the time series .DELTA.P[n] is computed
over a second sliding window to accentuate the activity of
higher-amplitude epochs in the preprocessed and fused inputs,
sufficiently more than the activity of lower-amplitude epochs. The
fourth-power indicator (FPI) is then given by, 5 FPI ( n ) = 1 N 2
i = n - N 2 + 1 n P ( i ) 4 ,
[0142] where N2 is the size of the new sliding window over the time
series .DELTA.P[n]. This second sliding window is chosen equal to
10 points, but can be another value. FIG. 17 shows the FPI in one
of the patients analyzed. The prediction ability of this feature
can be noticed in this figure. In this figure, the FPI from four
preictal and four interictal IEEG segments is shown from top to
bottom respectively. The dotted horizontal line on each plot
represents a hypothetical threshold that when surpassed is
considered as an indication of pre-seizure stage. The lines with
arrows are used to point out the sleep-awake cycles (sac), the
letters in the graph have the following meaning: a stands for
awake, d for drowsy, and s for asleep. There are moments during the
first four preictal segments when the hypothetical threshold is
surpassed suggesting a relationship between this feature and the
oncoming seizure event Only one baseline record yields false alarms
(the bottom one).
[0143] Average Nonlinear Energy or Moving Average Nonlinear
Energy
[0144] The nonlinear energy (NE) operator arises in the area of
signal processing and communications. It was first proposed by
Maragos et al. ("On Amplitude and Frequency Demodulation Using
Energy Operators", IEEE Trans. on Signal Processing, vol. 41, no.
4, pp. 1532-1550) as an AM-FM demodulator and later applied as a
spike detector. The square root of the NE operator was shown to
approximately track the product of the amplitude envelope and the
instantaneous frequency of sine wave signals with time-varying
amplitude and frequency. This definition was made by Maragos et al.
under the assumptions of: (1) the bandwidth of AM or FM information
signals is smaller than the carrier frequency; (2) noise free
signals; (3) AM modulation is less than 100%, and FM modulation is
less than 1 (.omega..sub.m/.omega..sub.c<1, where .omega..sub.m
is the modulating frequency and .omega..sub.c is the carrier
frequency). Therefore, implicit assumptions, when using this
feature, are that the brain signals can be modeled as a summation
of sinusoids with different amplitude and frequency modulation,
where the bandwidth of each AM or FM part is smaller than the
corresponding carrier. A possible physiological interpretation is
to consider each brain signal as the sum of several nonlinear
time-varying oscillators within the terminal contact area of the
electrode. As is known, neuron signals are FM modulated; therefore,
the many thousands of neuron voltages recorded can be divided into
groups representing each oscillator. Neuron signals with the same
carrier frequency and FM message will belong to the same group
(same oscillator); and hence, will add up their tuned signals to
produce the oscillator output. Thus, obviously, each of the
oscillators would represent the response produced by thousands of
neurons oscillating at the same frequency and transmitting the same
FM information. There will be as many oscillators as there are
different carrier frequencies and FM messages present. The AM
component is determined by the number of neurons contributing to
each oscillator. The more neurons that are tuned to the same
frequency, the larger is the amplitude of the oscillator, creating
the effect of an AM modulation. This hypothesis of multiple neuron
responses adding up to each oscillator output seems reasonable
considering that the NE operator makes no assumptions regarding the
source of the AM and FM signals.
[0145] The NE operator is computed according to the expression:
NE[n]=x.sup.2[n]-x[n-1]x[n+1].
[0146] The NE operator as well as the features derived from it, are
instantaneous features in the sense that they provide one value for
each value of the original data. Therefore, the values of the
nonlinear energy feature are subject to a second level of
extraction where they are weighted with a rectangular window or any
other window shape; their mean value is then calculated and called
average nonlinear energy. The length of this window is optimized
for the data set of each patient according to the procedure
described in FIG. 18 and illustrated for one of the features in
FIG. 19. The average nonlinear energy is obtained as follows, 6 ANE
[ k ] = 1 N n = 1 + ( k - 1 ) ( N - D ) k ( N - D ) + D NE [ n
]
[0147] where:
[0148] ANE[k] is the average nonlinear energy at time k,
[0149] N is the window length optimized for the data of each
particular patient,
[0150] D is the overlap in number of points,
[0151] k is a discrete time index equal to 1, 2, 3, . . .
[0152] It is observed that instead of using a rectangular window,
by utilizing an exponential window, the results can be enhanced.
This occurs because the feature values nearer to the seizure onset
(more recent ones) are emphasized more than the values that
occurred earlier. The exponentially weighted average nonlinear
energy (WANE) is found by: 7 WANE [ k ] = 1 N n = 1 + ( k - 1 ) ( N
- D ) k ( N - D ) + D NE [ n ] w [ n ] , w [ n ] = f s N - n / ( 2
f s ) ,
[0153] where:
[0154] w[n] is the exponential window used,
[0155] .function.s is the sampling frequency of the data signal
(typically 200 Hz).
[0156] FIG. 20 shows the WANE signal for a pre-seizure and baseline
record from the same patient. In this figure two bursts of enery
can be observed around 25 and 5 minutes before the UEO in the
preictal segment not present in the baseline segment. This feature
yielded similar results across the patients studied.
[0157] Thresholded Nonlinear Energy (TNE)
[0158] From the above expression for average nonlinear energy, the
thresholded nonlinear energy (a binary sequence) is derived as
follows:
TNE[n]=.theta.(NE[n]>th.sub.1),
[0159] where th.sub.1 is a threshold that is adjusted depending on
the patient as indicated in the following expression, and .theta.
is the Heaviside function also known as the step function. 8 th 1 =
C N B N k k = 1 N B i = 1 N k x k ( i ) ,
[0160] where N.sub.B is the number of records, N.sub.k is the
number of points in each record, X.sub.k(i) is the ith value of the
NE feature on record k, and C is a constant empirically selected to
be 1.5 after an ad-hoc estimation. This constant can be adjusted on
a patient basis.
[0161] Duration of Thresholded Nonlinear Energy
[0162] The duration in an "on" state of the time series TNE(n) is
determined by counting the number of consecutive ones, and creating
a new sequence or feature, whose values are zero except at the end
of stream of ones in the TNE(n) sequence, where this new sequence
takes a value equal to the number of consecutive ones found in that
stream of the TNE(n) sequence. FIG. 21 illustrates how this feature
can provide encouraging results from its behavior in eleven
one-hour segments that indicate a clear distinguishability between
preictal and no preictal portions of data up to 50 minutes prior to
the UEO. Further analysis is required to determine how long in
advance this difference becomes clear.
[0163] Ratio of Short and Long Term Power or any other Feature
[0164] This feature corresponds to a second level of feature
extraction where once the average power is obtained, two more
moving averages of the power are calculated over time for different
sliding window sizes. In one case the window length is long and in
the other it is short corresponding to the long term power and
short term power, respectively. The ratio of these two is taken and
assigned to the current time the feature is being computed. A
variation of this feature includes determining when the short term
power goes above or below an adaptive threshold obtained from the
long term power. The same ratio or threshold crossing between a
short and a long term feature can be computed for any other feature
from any of the domains mentioned in this invention. The duration
and magnitude by which the short term feature exceeds the adaptive
threshold can also be quantified in a third level of extraction.
FIG. 22 shows the times as well as the magnitude by which the short
term energy of the 4.sup.th wavelet coefficient exceeded the 20%
value of the long term energy of the same coefficient. These
results were computed over five one-hour preictal IEEG segments
from one epileptic patient. The continuous line indicates how a
continuous adaptive threshold classifier based on a duration and
magnitude of the difference between the short and long term energy
can provide a prediction for a time horizon around two minutes
utilizing only this feature. It is expected that when more features
are added into the analysis, the performance will improve. Twelve
one-hour baselines where also analyzed yielding a total of 8 FPs
under this raw classification scheme, which was used only for
evaluation purposes.
[0165] Fractal Dimension of Analog Signals
[0166] The fractal dimension (FD) of a waveform can be computed
over time by using Katz's algorithm, with very good results for
early detection of the UEO. The FD of a curve can be defined as: 9
D = log 10 ( L ) log 10 ( d )
[0167] where L is the total length of the curve or sum of distances
between successive points, and d is the diameter estimated as the
distance between the first point of the sequence and the point of
the sequence that provides the farthest distance. Mathematically
speaking, d can be expressed as:
d=max(x(1), x(r)).
[0168] Considering the distance between each point of the sequence
and the first, point r is the one that maximizes the distance with
respect to the first point.
[0169] The FD compares the actual number of units that compose a
curve with the minimum number of units required to reproduce a
pattern of the same spatial extent. FDs computed in this fashion
depend upon the measurement units used. If the units are different,
then so are the FDs. Katz's approach solves this problem by
creating a general unit or yardstick: the average step or average
distance between successive points, .alpha.. Normalizing distances
in the equation for D by this average results in, 10 D = log 10 ( L
/ a _ ) log 10 ( d / a _ )
[0170] Defining n as the number of steps in the curve, then
n=L/.alpha., and the previous equation can be written as: 11 D =
log 10 ( n ) log 10 ( d L ) + log 10 ( n ) .
[0171] The previous expression summarizes Katz's approach to
calculate the FD of a waveform. A great deal of repeatability has
been observed with this feature and with the FD of binary signals
across records from the same patient and even across patients
("Fractal Dimension characterizes seizure onset in epileptic
patients", 1999 IEEE International Conference on Acoustics, Speech,
and Signal Processing, by Esteller et al.).
[0172] Fractal Dimension of Binary Signals
[0173] The FD of digital or binay signals is calculated using
Petrosian's algorithm. It uses a quick estimate of the FD. Since
waveforms are analog signals, a binary signal is derived from the
analog input signal by obtaining the differences between
consecutive waveform values and giving them the value of one or
zero depending on whether or not their difference exceeds a
standard deviation magnitude or another fixed or adjustable
threshold. The FD of the previous binary sequence is then computed
as: 12 D = log 10 n log 10 n + log 10 ( n n + 0.4 N )
[0174] where n is the length of the sequence (number of points),
and N.sub..DELTA. is the number of sign changes (number of
dissimilar pairs) in the binary sequence generated.
[0175] Curve Length
[0176] Inspired by Katz's definition of FD, the curve length is a
feature that resembles the FD but runs faster because it is easier
to implement in real time. It is computed as follows: 13 C L ( n )
= k = n n + N abs [ x ( k - 1 ) - x ( k ) ]
[0177] where CL(n) is the running curve length of time series x(k),
N is the sliding observation window, and n is the discrete time
index. This feature plays an important role for early detection of
seizure onsets.
[0178] Frequency Domain Features
[0179] This category includes all features that contain some
information regarding the frequency domain, such as frequency
content of the signal, frequency content in a particular frequency
band, coherence, ratio of the frequency energy in one band with
respect to another, crossings of the mean value in the power
spectrum or in the time series, etc.
[0180] Power Spectrum
[0181] The spectrum is estimated using Welch's average periodogram,
which is the most widely used periodogram estimation approach.
Welch's average periodogram is given by, 14 P ^ w ( f ) = 1 P p = 0
P - 1 P xx ( p ) ( f ) , where : P xx ( p ) ( f ) = 1 UDT X ( p ) (
f ) 2 , U = T n = 0 D - 1 w 2 [ n ] , X ( p ) ( f ) = T n = 0 D - 1
x ( p ) [ n ] exp ( - j2 f n T ) , x ( p ) [ n ] = w [ n ] x [ n +
pS ] ,
[0182] P is the number of sub-segments analyzed inside each input
segment,
[0183] 0<p<P-1 is the index range of segments,
[0184] .function. is the frequency,
[0185] D is the length of the periodogram window,
[0186] w[n] is the Hamming window,
[0187] x.sup.(p)[n] is the weighted pth sub-segment,
[0188] x[n] is the data segment,
[0189] T is the sampling period,
[0190] S is the number of samples shifted as the window moves
through the input segment.
[0191] The power spectrum is computed using the running observation
window to visualize the spectrum changes over time. Even though
this feature is evaluated to characterize the bandwidth of the IEEG
signals and to compare it during ictal, preictal and interictal
epochs, it is really used to derive the power on different
frequency bands as described below.
[0192] Power on Frequency Bands
[0193] Once the power spectrum is estimated, the power on four
frequency bands can be analyzed: delta band (lower than 4 Hz),
theta band (between 4 and 8 Hz), alpha band (between 8 Hz and 13
Hz) and beta band (between 13 Hz and 30 Hz). The power on each band
is computed as the area under the spectrum for the corresponding
frequency band (i.e., the integral of each band). The following
equation represents the computation: 15 P i = 1 P T k = f 1 f 2 X (
k ) ,
[0194] where Pi is the power on the frequency band i, i can be
either: delta, theta, alpha or beta band, .function..sub.1 and
.function..sub.2 are the low and high frequency indices of the band
under consideration, k is the discrete frequency index, X(k) is the
power spectrum, and P.sub.T is the total power (integral of X(k) ).
FIG. 23 illustrates the power on the frequency band between 8 and
13 Hz (alpha) for a 50-minute preictal segment and a baseline
segment. There is a clear difference in the power in this frequency
band that between the two segments is also observed in the other
segments analyzed. Around three minutes before the UEO a peak value
is reached in the power of this frequency band (see FIG. 23).
[0195] Coherence
[0196] This is the signal processing name for the cross-correlation
between two frequency spectra. It is calculated to explore the
issue raised by some researchers, regarding a frequency entrainment
or neural synchronization between the focal area and other cortical
sites prior to seizure onset. Channels from the focal region and
other cortical sites of the brain have been reported to exhibit
some alignment in their phases for different features as the
seizure approaches. The coherence between the focal channel and its
homologous contralateral site is a good method for analyzing neural
synchronization. It is computed using a practical method to
determine the coherence between two signals, as indicated by 16 C
xy ( k ) = k P xx ( k ) max i { P xx ( i ) } P yy ( k ) max i { P
yy ( i ) } ,
[0197] where Pxx is the power spectral density of x[n], and Pyy is
the power spectral density of y[n]. Note that C.sub.xy is the
vector given by the product of each frequency value of the maximum
normalized power spectral density of x, {P.sub.xx(i)}, and the
maximum normalized power spectral density of y, 17 max i { P yy ( i
) } .
[0198] Mean Crossings
[0199] This feature counts the number of times the signal crosses
the mean value of the window segment under analysis. As the running
window slides over the data, the number of crossings is calculated
for each window.
[0200] Zero Crossings
[0201] The number of times the input signal crosses the zero value
is counted within a pre-defined sliding observation window.
[0202] Wavelet Domain Features
[0203] Intuitively, wavelet analysis can be considered as a
variable-length windowing technique. In contrast with the
short-time Fourier transform, wavelet analysis can study phenomena
that is localized in time. This possibility of associating a
particular event characterized by a frequency component, a
disturbance, etc., to a time span, is one of the major advantages
of wavelet analysis. Wavelets are waveforms of limited duration
with zero average value and a tendency to be asymmetric. In
contrast, sine waves have smooth and symmetrical shape and infinite
duration. The short-time Fourier analysis uses a time-frequency
region rather than the time-scale region used by wavelet analysis.
While the Fourier approach uses a fixed window length that
determines the resolution, in the wavelet analysis different window
lengths are used (i.e, different scales), such that if the interest
is in low frequencies, long time windows are appropriate and the
opposite holds true for high frequencies. Another important concept
that differentiates both types of analysis is that the Fourier
transform breaks the data signal into sine waves with different
frequencies, and the wavelet transform breaks the data signal into
shifted and scaled versions of the mother wavelet used.
[0204] Spike Detector
[0205] There has been much discussion in the technical literature
regarding the possibility of a relationship between the presence of
spikes on the EEG signal and the occurrence of a seizure. Aimed
toward testing this hypothesis, a spike detector has been
developed. Initially, the NE operator was computed, but only high
amplitude spikes were detected, while low amplitude spikes were
missed. The spike detector developed in this invention utilizes a
"prototype spike" as the mother wavelet. A set of spikes is
randomly chosen from the patient, and by aligning and averaging
these spikes, a "prototype spike" is created and denoted as the
mother wavelet. This prototype spike is patient-tuned. Using the
running window method the inner product of this "prototype spike"
and the data is computed; once it reaches a value higher than a
pre-established threshold a spike is detected. FIG. 24 illustrates
the behavior of the spike detector for a segment of IEEG. From this
figure, the spike detection is clear disregarding the spike
amplitude. FIG. 25 shows the spikes detected over time in eight
one-hour records for four preictal and four baselines. Each
vertical line denotes a spike detected, the amplitude of the
vertical line increases in proportion to the excess of the inner
product over the threshold. From this figure, it is clear how a
second level of extraction computing the density of spikes over
another running window can distinguish between the preictal and
baseline records tens of minutes prior to the seizure.
[0206] Density of Spikes over Time
[0207] Using the spike detector developed, in a second level of
extraction, a threshold is used to count the number of spikes that
fall in the running window over time. Results presented in FIG. 25
are encouraging to process the prediction of UEO with features of
this nature.
[0208] Absolute Value of the 4th Wavelet Coefficient
[0209] Results with several wavelets have been examined by visual
inspection. Among the mother wavelet results observed, the one that
provided the best visual separation between classes is the result
obtained with Daubechies 4. The wavelet transform is run over the
data for four or more different scales. The scale that provides the
best distinguishability between the preictal and the ictal class is
selected. FIG. 26 presents 3.5-minute epochs of five seizures from
the same patient, extracted for the one-hour preictal records
analyzed. A clear elevation starts between one minute and a
half-minute before the seizure UEO. Using a basic threshold
classifier a typical prediction time based on only this feature
would be around two minutes. Twelve one-hour baseline segments were
also analyzed using this feature in this patient with the same
simple threshold classifier, yielding only one FP. This seems to be
a good feature to use as part of the feature library. Similar
results were found across patients. This feature was initially
analyzed for 6-minute records instead of 1-hour records, because it
generates one feature value for each IEEG sample, therefore, it has
no data compression. However, after the second level of extraction
is conducted, where a running window is slid over the wavelet
coefficients and the mean of their absolute value is calculated for
the feature values within each window, it resulted in data
compression, while preserving most of the feature information and
decreasing variability. The window length varied from patient to
patient, depending on the result of the window size optimization
described below.
[0210] Statistics and Stochastic Processes
[0211] From the huge variety of features in the statistical domain,
the mean frequency index, the cross-correlation, and the coeffients
of an autoregressive (AR) model are among the ones included in the
feature library of the present invention.
[0212] Mean Frequency Index
[0213] This is a measure of the centroid frequency, calculated as
follows: 18 m f = f s N i = 1 N / 2 ( i - 1 ) x i i = 1 N / 2 x i
,
[0214] where .function.s is the sampling frequency, N is the length
of the IEEG segment, and x.sub.i is the magnitude of the power
spectrum.
[0215] FIG. 27 shows the mean frequency index of a seizure and a
baseline record over time for a window length of 2000 points or
equivalently 10 seconds. The vertical line at time zero emphasizes
the UEO time. It is clear from this figure, that the mean frequency
can be a useful feature for seizure UEO prediction/detection
considering the small elevation of the average frequency as the
seizure approaches which is not observed during baseline periods
away from icial activity. Note the presence of sudden periodic
peaks above 20 Hz starting around 12 minutes before the seizure
UEO. Other records in the database exhibited a similar behavior.
This feature may be enhanced to increase the distinguishability
between preictal and no-preictal records, by either utilizing a
different shifting and window length, or by an additional
processing at a third level of extraction, such as averaging,
detection of the maximum value over a third running window, ratio
of short term versus long term frequency index, etc. The clear
issue is that the mean frequency index may provide a smoother
feature with less variability over time and better results.
[0216] Cross-correlation
[0217] The consideration of this feature is motivated for the same
reasons that encouraged the coherence analysis between homologous
contralateral channels. The cross-correlation can reflect the
degree of similarity between different channels, therefore, if a
synchronization takes place, at some point before the seizure, this
feature should be able to sense a change in that direction. The
mathematical expression to compute the cross-correlation is given
by 19 R xy ( m ) = 1 N n = 0 N - m - 1 x [ n + m ] y * [ n ] , for
0 m N - 1.
[0218] The running cross-correlation is computed for each sliding
observation window used according to the window selection procedure
summarized in the flowchart of FIG. 18 and exemplified in FIG. 19.
Each time the cross-correlation is calculated, a sequence of values
is obtained for the different lags, the maximum cross-correlation
value from all the different lags is the one kept over time for the
generation of this feature.
[0219] Autoregressive (AR) Coefficients or Linear Prediction
Coefficients
[0220] A time series model often used to approximate discrete-time
processes is the AR model whose time domain difference equation is:
20 x [ n ] = - k = 1 p a [ k ] x [ n - k ] + u [ n ] ,
[0221] where p represents the AR model order. From this expression,
it is clear that the sample at time n is being estimated from the p
previous samples and the present input. In time series analysis
where no input is available, u[n] is considered as white gaussian
noise error between the real present sample x[n] and the sample
estimated without input. A forward linear predictor is used to
estimate the AR coefficients. Defining the error variance as
.rho.=E {.vertline.e.sup..function.[n].vertline..sup.2},
[0222] where
e.sup..function.[n]=x[n]-{circumflex over
(x)}.sup..function.[n],
[0223] then, the forward linear prediction estimate is 21 x ^ f [ n
] = - k = 1 p a f [ k ] x [ n - k ] .
[0224] Computing the error variance from the error definition
above, and substituting the forward linear prediction estimate
yields the following equation
.rho.=r.sub.xx[0]+r.sub.p.sup.H.alpha..sup..function.+(.alpha..sup..functi-
on.).sup.Hr.sub.p+(.alpha..sup..function.).sup.HR.sub.p-1.alpha..sup..func-
tion.,
[0225] where:
[0226] .alpha..sup..function. is a vector with the AR
coefficients,
[0227] r.sub.p is a vector with the autocorrelation for lags 1 to
p,
[0228] and R.sub.p-1 is the autocorrelation matrix,
[0229] H represents the conjugate transposed.
[0230] The AR coefficients can be found by minimizing the last
equation. Preliminary results suggest this feature has potential
for prediction.
[0231] Information Theory Features
[0232] Features from the information theory domain are available in
the feature library, including the entropy as originally defined by
Shannon, and the mutual information function. It has been
hypothesized that the level of organization changes before, during
and after a seizure; thus, these features must be analyzed to
explore this possibility.
[0233] Entropy
[0234] Entropy is a measure of "uncertainty," and is heavily used
in the information theory field. The more uncertainty there is
regarding the outcome of an event, the higher is the entropy. The
entropy is computed by using: 22 H = - i = 1 20 p d f ( i ) log 2 (
p d f ( i ) ) ,
[0235] where pdf in this setting stands for the probability
distribution function. It is found by dividing x (i.e., IEEG data
segment) into 20 different amplitude containers, determining how
many values of x are in each container, and normalizing by the
number of values in the observation window. Thus, the pdf is a
20-bin histogram normalized to represent discrete probabilities.
Note that i in the above expression indicates the container number.
A different number of containers can be chosen depending on the
length of the sliding observation window used.
[0236] Average Mutual Information
[0237] This feature is explored with the idea of finding a relation
between the information in the focal channel and the homologous
contralateral channel. This feature is also considered as a
nonlinear cross-correlation function. The mathematical expression
used for the computation of the average mutual information is: 23 I
AB = a i , b j P AB ( a i , b j ) log 2 [ P AB ( a i , b j ) P A (
a i ) P B ( b j ) ] ,
[0238] where:
[0239] P.sub.AB is the joint probability distribution of A and
B,
[0240] P.sub.A is the probability distribution of A, and
[0241] P.sub.B is the probability distribution of B.
[0242] Window Length Selection
[0243] Several factors are taken into account when determining the
window length to be used in the analysis. Among them are data
stationarity, data length required to compute the features,
sampling frequency, maximizing the distinguishability between
preictal and ictal segments, and maximizing the accuracy in the
prediction time. A compromise has to be achieved between the
requirement of a window sufficiently long to compute specific
features and a window short enough to assume data stationarity. An
IEEG segment of tens of seconds can be considered quasi-stationary,
depending on the patient's behavioral state. This depends also on
the type of input signal under consideration, for example chemical
concentrations may be considered quasi-stationary over a longer
time frames.
[0244] An original methodology for selecting the window size is
introduced here. This methodology arises as an answer to the issues
of how to effectively select the window size to compute specific
features and how to create the feature vector when the features
extracted have different lengths. These questions emerged during
the development of the feature extraction stage of this invention.
The goal of this technique is to maximize the distinguishability
between the preictal/ictal class and baseline class. The processing
logic of FIG. 18 and results of FIG. 19 summarize the procedure. In
this scheme, each of the features pre-selected is computed for
different sliding window sizes. The k-factor is used as the
performance criteria that guides the window size selection by
quantifying class-separability and variance, however any other
performance measure suitable for this purpose can be used.
[0245] Ninety different window sizes or less are selected within
the range of 50 points (0.25 seconds) to 9000 points (45 seconds).
This window range is selected to include the maximum window size to
satisfy quasi-stationarity of the data segments and the minimum
window size required to compute the feature. All these windows are
shifted according to either of the following two criteria. The
windows are shifted by a fixed shift of 90 points (0.45 seconds)
along the input sequence, or by the shift that corresponds to
preserving a 50% overlap in the running window methodology. The
running window method described earlier is used to generate the
features. These 90-point shifts or 50% of window length shifts fix
the minimum prediction time to 0.45 seconds or to the time shift
that corresponds to the 50% of the window size used. The maximum
delay in the UEO detection is also the same as the time shift,
assuming optimal features, as those capable of detecting the
seizure onset as soon as one sample of the ictal input data is
within the sliding window. There is also a trade-off between this
window shifting or time resolution and the storage capacity of the
system. The shorter this time resolution or the smaller the window
shifting, the greater the memory space required.
[0246] After each feature is computed for the different windows,
the k-factor in the following equation is computed as a measure of
effectiveness of each feature. 24 K = 1 - 2 ( 1 2 + 2 2 ) / 2 ,
[0247] where:
[0248] K is the k-factor (measure of effectiveness of the
feature),
[0249] .mu..sub.i is the mean of feature for class i,
[0250] .sigma..sub.i.sup.2 is the variance of feature for class
i.
[0251] Around 20% of the available preseizure records are used to
determine the best window length to use. For each pre-seizure
record used, the window size corresponding to the maximum k-factor
is chosen to precede the analysis. Then, a verification follows to
confirm that the window lengths that maximize the k-factor in each
record are clustered around some value. The center of the cluster
of "optimal" window lengths is chosen as the window length for the
feature under consideration. FIG. 19 illustrates the variation of
the k-factor for the fractal dimension feature, as the window size
is changed for four different seizure records. The so-called
"optimal" window length is within approximately 1000 and 1500
points in this case.
[0252] Typically, the window sizes that maximize the k-factor are
different for each feature. Therefore, a strategy is required to
allow the creation of feature vectors from features extracted with
different sliding window sizes and sometimes also with different
window shiftings, which implies that the features do not coincide
in time and have different time spans between consecutive values.
One way to obtain a perfect time alignment and identical time span
across features, is by satisfying the following two conditions. The
first condition guarantees the same time span for consecutive
values on all the features. This is achieved by making the
observation window displacement equal for all the window sizes on
all the features. The second condition requires the alignment of
all the observation windows with respect to the right border of the
longest window, as shown in FIG. 28. The effect of applying equal
displacement of the observation window even for features with
different window sizes is that the number of overlapping points on
each observation window will change from feature to feature, while
the shifting points will remain constant. Therefore, as a way to
preserve the percentage of overlap for all the features or to even
have different percentages of overlap and different shiftings
(making the system more general), a second alternative can be
followed. It is to align the features in time by resampling them.
In this form, the features with less samples can be upsampled by
adding as many values as needed. For example, if the upsampling is
by three, then each value of the feature sequence will be repeated
twice.
[0253] Using any of the two approaches described, historical and
instantaneous features can be combined by extracting historical
features from the instantaneous features utilizing a shift of
one-feature-sample for the observation window, upsampling if
necessary to achieve a correct time alignment of the historical
features and the instantaneous ones. Intuitively, this type of
approach can outperform those that rely only on instantaneous
features. An example is the use of delta features in speech
processing.
[0254] When the feature-parameter approach is used, the feature
selection is a required procedure performed by the supervisory
control 400 that involves the extraction of features within the
feature library and the analysis to select the "optimal" set of
features.
[0255] Feature selection deals with determining the smallest subset
of features that satisfies a performance criterion once the set of
candidate features has been extracted. Candidate features must be
ranked by their effectiveness to achieve class separability. This
implies that feature selection is also a feature optimization
problem, where an optimal feature subset has to be chosen from the
combinatorial problem of finding a subset with the best M features
out of N original features. Several issues must be considered for
the feature selection, such as minimization of numerical
ill-conditioning, maximization of discrimination among classes,
maximization of orthogonality, selection of classifier topology,
and computational loading for real-time implementation.
[0256] Typical causes of ill-conditioning are large differences in
the orders of magnitude between pairs of features, statistical
correlation between any pair of features, a large number of
features, and a small number of training feature vectors. To reduce
ill-conditioning problems, features must be normalized so that
different scaled feature values will have the similar mean and
variance. A basic normalization scheme can be achieved by using the
expression: 25 f k ( n ) = f k ( n ) - k k ,
[0257] where:
[0258] .function..sub.k (n) is the nth sample from feature k,
[0259] .function..sub.k (n) is the nth sample normalized from
feature k,
[0260] .mu..sub.k is the average over all feature samples from all
classes,
[0261] .sigma..sub.k is the standard deviation over all feature
samples from all classes.
[0262] Thus, .mu..sub.k and .sigma..sub.k are computed as: 26 k = 1
N i = 1 N f k ( i ) and k = 1 N - 1 i = 1 N ( f j ( i ) - k ) 2
.
[0263] The implementation of the previous normalization scheme in
an on-line fashion requires the computation of the average and
standard deviation over a long term running window that covers part
of the feature history The length of the window for computing the
parameters required for feature normalization depends on the
probability time horizon under consideration. A typical window may
be ten times or more the time horizon analyzed. There is a
trade-off between this historical window and the memory available
within the implantable device.
[0264] In addition, some correlation studies can be helpful to
select a final group of features that synergistically contributes
to the onset detection task. These can be performed by the
supervisory control at the coordination level.
[0265] The feature vector optimization is performed initially in
four major steps following a scheme of multi-dimensional feature
optimization. This procedure can evolve into a single-dimensional
feature optimization, if the correlation and complementary nature
of the features involved is qualitatively acceptable implying that
the final feature set obtained by both procedures (single and
multi-dimensional) is about the same. The fundamental aspects of
the multidimensional scheme that can also be used are summarized in
the following steps:
[0266] Step 1: An initial basic pre-selection is used to discard
features with evidently inferior class separability, by assessing
the mean and standard deviation differences in data segments from
preictal and no-preictal conditions.
[0267] Step 2: Individual feature performance is evaluated using
one or more criteria for every feature that is not discarded during
the initial basic pre-selection.
[0268] Step 3: Features are ranked according to their performance
measure by an overlap measure criteria and then a modified version
of an add-on algorithm combined with heuristics is used to select
the final feature set.
[0269] Step 4: Two-dimensional feature spaces are constructed and
evaluated to validate qualitatively the implicit assumption of
complementarity and low correlation among the final feature
set.
[0270] Considering that the performance of single dimensional
feature optimization is slightly lower (typically between 3 and 8%)
than its multidimensional counterpart, it provides an acceptable
optimization. However, if the feature correlation is such that the
features are not complementary, a multidimensional feature
optimization approach is preferred. A computational assessment of
the feature space is utilized to evaluate the complementarity among
the features involved. The previous steps and considerations are
followed by the internal program residing in the high level
supervisory control 400 at the coordination layer.
[0271] A measure of overlap between the two classes involved
(pre-seizure and no pre-seizure class) can be achieved on the
estimated conditional probability distribution function (PDF) of
the feature under analysis for each class. FIGS. 29A and 29B
present two examples of curves proportional to the feature PDFs
estimated directly from the data set for each class in two patients
of the database. The curve with the peak in the left is
proportional to the estimated PDF of the weighted fractal dimension
(WFD) obtained from the actual data values of the WFD in no
pre-seizure segments that include baseline records. This can be
expressed mathematically as p(x.vertline.NPS), which means the PDF
of feature x (in this case the WFD) given that the feature data
belongs to the no pre-seizure class (NPS). The curve whose peak is
in the right side of the figure, is proportional to the estimated
PDF of the WFD given data from the pre-seizure class
(p(x.vertline.PS) ). The pre-seizure (PS) class is defined as the
segments whose length is identical to the time horizon under
analysis and whose ending point is right before the seizure UEO.
The two graphs correspond to two different patients studied. During
the analysis of the data, it was observed that the PDF depicted by
the curve whose peak is in the right side of FIG. 29B, if plotted
including the whole seizure time (about 3 min.) as if it were from
the preictal class, then the PDF becomes multimodal. In fact, this
can be inferred by looking at the trend of the left curve for low
values of the WFD in FIG. 29B. This was not always the case in
every patient, but it was an interesting observed behavior.
[0272] The overlap between the two classes is assessed by
integrating the shaded region in FIGS. 29A and 29B, as stated
according to:
ov=.intg.min (p(x.vertline.PS), p(x.vertline.NPS))dx,
[0273] where:
[0274] ov is a measure of overlap between the feature classes,
[0275] p(x.vertline.NPS) is the PDF of feature x given no seizure
onset class,
[0276] x is a variable representing the feature for both
classes,
[0277] p(x.vertline.PS) is the PDF of feature x given the seizure
onset class.
[0278] Note that the better the class distinguishability for a
particular feature, the lower this overlap measure. The overlap
measure is very general in the sense that it works under
multi-modal distributions. Using the previous equation the features
can be ranked individually, preparing the ground to start the
multiple-dimension feature optimization.
[0279] In those problems where the class boundary is very complex
and a substantial overlap is obtained in the one-dimensional
feature space, a multidimensional feature optimization is the path
to follow. This type of approach is computationally more intensive
than single-dimension feature optimization, but it has the
advantage of compensating for the correlation among features.
[0280] FIGS. 30 and 31 show the qualitative results from the
construction of the 2-D feature space for some of the final pairs
of features in the final feature set of one of the patients
studied. This reinforces the idea that features are complementary.
The top graphs in FIGS. 30 and 31 correspond to the 1-D feature
spaces of each of the three features selected, plotted in a 2-D
graph for visualization purposes. The representation of each 1-D
plot as a 2-D plot is achieved by assigning a random value to
correspond with each feature value. In both figures it is observed
how combined features enhance the performance by decreasing the
overlap between the classes.
[0281] Following the single dimensional feature optimization
approach for all the patients studied, the final feature set
coincided for almost all the patients when using the overlap
measure and when using other performance criteria such as the
Fisher discriminant ratio (FDR). The overlap criteria provides a
more reliable distinguishability measure between the classes since
the FDR is a linear measure based on the 1st and 2nd statistical
moments while the overlap measure is based on the PDFs that
implicitly contain the information of all the statistical moments.
Therefore, even when the FDR measure suggested a slightly different
final feature set (where at most, one of the features was
different), the overlap measure is chosen as the criterion to
determine the final feature selection.
[0282] Patients with Multiple Focus Regions
[0283] In patients where the seizures arise from more than one
focal region, multiple electrodes are implanted in each region. The
approach followed in these cases is the same as that described
above, with two possible variations regarding the fusion of
information. In one variation, the input signals from adjacent
electrodes are subtracted forming a bipolar signal, and then
bipolar signals from different focus regions are combined at the
data level; in the other variation, the input signals are combined
at the feature level. The second variation implies that features
computed with the same algorithm and perfectly coincident or
aligned in time are combined into a single feature by using a
nonlinear procedure. Similarly, the first variation implies the
combination of the intracranial EEG data or any other sensor data,
before or after the preprocessing stage, into a single data stream.
A method for the nonlinear combination of the input signals either
at the data or at the feature level is to take the maximum of the
two or more signals at every sample time. Besides this nonlinear
combination, there are many other techniques that can be used to
combine or fuse these signals or channels.
[0284] The combination of signals at the data and/or feature level
can also be performed in patients with a unique focal region, where
the complementarity among the signals or features from electrodes
placed in different regions enhances the prediction results.
[0285] Analysis/Classification
[0286] A classifier can be viewed as a mapping operator that
projects the M selected features contained in the feature vector
onto a d-dimensional decision space, where d is the number of
classes in the classification problem. In the classification
problem under investigation for this invention, d=2 and M is chosen
typically to be within the range of one to six. It is definitely
true that the feature extraction and selection plays a crucial role
in the classification results; however, it is highly important to
select a classifier architecture suitable to the underlying feature
distribution to obtain better performance recognition.
[0287] As a benchmark and proof-of-concept, a radial basis neural
network (RBNN), without the usual iterative training algorithms,
has been used. Particularly, a Probabilistic Neural Network (PNN)
has been used within this invention for its suitability for
classification problems and its straightforward design. The PNN is
a nonparametric classifier, and as such it does not make
assumptions regarding the statistical distribution of the data.
This neural network is also called kernel discriminant analysis, or
the method of Parzen windows.
[0288] FIG. 32 illustrates the PNN architecture which corresponds
to one of the embodiments of this invention. In other embodiments,
different neural networks can be used or a combination of a neural
network with a fuzzy system can be utilized. The weights used at
the hidden layer of the PNN are directly the training vectors used.
As can be seen in FIG. 32, this type of network requires one node
for each training vector W.sub.k, which represents a major
disadvantage since the amount of computation involved to reach a
classification, slows down its operation. Increasing the memory
capacity such that the PNN can be wired (run in parallel) can
decrease the computational burden and accelerate the
classification. On the other hand, an advantage of the PNN is its
convergence to an optimal Bayesian classifier provided it is given
enough training vectors, and under equiprobable spherical class
covariances for the particular implementation used in this
invention.
[0289] The architecture illustrated in FIG. 32 corresponds to the
particular case of a two-class problem, with three-dimensional
feature vectors,
x=[x.sub.1x.sub.2x.sub.3].sup.T.
[0290] Every weight W.sub.k,j in the hidden layer is the jth
component of the kth feature vector in the training set, where the
kth feature vector is given by
W.sub.k=[w.sub.1,kw.sub.2,kw.sub.3,k].sup.T
[0291] where k=1,2, . . . , n and n is the number of feature
vectors (patterns) in the training set. The output layer estimates
the probability of having a seizure, given the input feature
vector. This translates into the probability that the input signals
belong to he pre-seizure/seizure class (preictal class) or to the
non-pre-seizure class (baseline class), given the input feature
vector, and is mathematically represented by:
P.sub.1=P(PS.vertline.x) and P.sub.2=P(NPS.vertline.x)
[0292] where PS is the "pre-seizure/seizure class" and NPS is the
"non-pre-seizure class". Matrix T contains the weights on the
output layer, which indicate the corresponding class of each
training feature vector, in the 1-of-k binary feature format, as
typical in supervised learning approaches like this.
[0293] This architecture can be perceived in two ways. In one
interpretation the Euclidean distance z.sub.k between each input
feature vector x and each of the training vectors w.sub.k is
computed at each node .parallel.x-w.sub.k.parallel. in the hidden
layer and passed through a Gaussian window
e.sup.-z.sup..sub.k.sup..sup.2.sup./.sigma..sup..sup.2, where
.sigma..sup.2 is a width parameter of the window. The second
interpretation is more from a neural network point of view, and
considers that each input feature vector x is evaluated at n
Gaussian windows with each one centered at a different training
feature vector w.sub.k, k=1, . . . , n, and with variance
.sigma..sup.2.
[0294] The present invention is realized in a combination of
hardware and software. Any kind of computer system or other
apparatus adapted for carrying out the methods described herein is
suited. A typical combination of hardware and software could be a
general purpose computer system with a computer program that, when
loaded and executed, controls the computer system such that it
carries out the methods described herein. The present invention can
also be embedded in a computer program product which includes all
the feature enabling the implementation of the methods described
herein, and which, when loaded in a computer system is able to
carry out these methods.
[0295] Computer program instructions or computer program in the
present context means any expression in any language, code, or
notation or a set of instructions intended to cause a system having
an information processing capability to perform a particular
function, either directly or when either or both of the following
occur: (a) conversion to another language, code, or notation; (2)
reproduction in a different material form.
[0296] In light of the above teachings, those skilled in the art
will recognize that the disclosed methods, formulas, algorithms,
and embodiments may be replaced, modified, or adapted without
departing from the spirit or essential attributes of the invention.
Therefore, it should be understood that within the scope of the
appended claims, this invention may be practiced otherwise than as
exemplified herein.
* * * * *